WorldWideScience

Sample records for regional demand forecasts

  1. Electricity demand forecasting techniques

    International Nuclear Information System (INIS)

    Gnanalingam, K.

    1994-01-01

    Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

  2. Forecasting world and regional aviation jet fuel demands to the mid-term (2025)

    International Nuclear Information System (INIS)

    Cheze, Benoit; Gastineau, Pascal; Chevallier, Julien

    2011-01-01

    This article provides jet fuel demand projections at the worldwide level and for eight geographical zones until 2025. Air traffic forecasts are performed using dynamic panel-data econometrics. Then, the conversion of air traffic projections into quantities of jet fuel is accomplished by using a complementary approach to the 'Traffic Efficiency' method developed previously by the UK Department of Trade and Industry to support the Intergovernmental Panel on Climate Change (). According to our main scenario, air traffic should increase by about 100% between 2008 and 2025 at the world level, corresponding to a yearly average growth rate of 4.7%. World jet fuel demand is expected to increase by about 38% during the same period, corresponding to a yearly average growth rate of 1.9% per year. According to these results, energy efficiency improvements allow reducing the effect of air traffic rise on the increase in jet fuel demand, but do not annihilate it. Jet fuel demand is thus unlikely to diminish unless there is a radical technological shift, or air travel demand is restricted. - Highlights: → Jet fuel demand is forecasted at the worldwide and regional level until 2025. → Regional heterogeneity must be considered when forecasting jet fuel demand. → World air traffic should increase by about 100% between 2008 and 2025. → World jet fuel demand is expected to increase by about 38% during the same period. → Technological progress will not be enough to decrease the world jet fuel demand.

  3. Regional demand forecasting and simulation model: user's manual. Task 4, final report

    Energy Technology Data Exchange (ETDEWEB)

    Parhizgari, A M

    1978-09-25

    The Department of Energy's Regional Demand Forecasting Model (RDFOR) is an econometric and simulation system designed to estimate annual fuel-sector-region specific consumption of energy for the US. Its purposes are to (1) provide the demand side of the Project Independence Evaluation System (PIES), (2) enhance our empirical insights into the structure of US energy demand, and (3) assist policymakers in their decisions on and formulations of various energy policies and/or scenarios. This report provides a self-contained user's manual for interpreting, utilizing, and implementing RDFOR simulation software packages. Chapters I and II present the theoretical structure and the simulation of RDFOR, respectively. Chapter III describes several potential scenarios which are (or have been) utilized in the RDFOR simulations. Chapter IV presents an overview of the complete software package utilized in simulation. Chapter V provides the detailed explanation and documentation of this package. The last chapter describes step-by-step implementation of the simulation package using the two scenarios detailed in Chapter III. The RDFOR model contains 14 fuels: gasoline, electricity, natural gas, distillate and residual fuels, liquid gases, jet fuel, coal, oil, petroleum products, asphalt, petroleum coke, metallurgical coal, and total fuels, spread over residential, commercial, industrial, and transportation sectors.

  4. Are demand forecasting techniques applicable to libraries?

    OpenAIRE

    Sridhar, M. S.

    1984-01-01

    Examines the nature and limitations of demand forecasting, discuses plausible methods of forecasting demand for information, suggests some useful hints for demand forecasting and concludes by emphasizing unified approach to demand forecasting.

  5. Worldwide satellite market demand forecast

    Science.gov (United States)

    Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

    1981-01-01

    The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.

  6. Tourism demand in the Algarve region: Evolution and forecast using SVARMA models

    Science.gov (United States)

    Lopes, Isabel Cristina; Soares, Filomena; Silva, Eliana Costa e.

    2017-06-01

    Tourism is one of the Portuguese economy's key sectors, and its relative weight has grown over recent years. The Algarve region is particularly focused on attracting foreign tourists and has built over the years a large offer of diversified hotel units. In this paper we present multivariate time series approach to forecast the number of overnight stays in hotel units (hotels, guesthouses or hostels, and tourist apartments) in Algarve. We adjust a seasonal vector autoregressive and moving averages model (SVARMA) to monthly data between 2006 and 2016. The forecast values were compared with the actual values of the overnight stays in Algarve in 2016 and led to a MAPE of 15.1% and RMSE= 53847.28. The MAPE for the Hotel series was merely 4.56%. These forecast values can be used by a hotel manager to predict their occupancy and to determine the best pricing policy.

  7. Guidelines for forecasting energy demand

    International Nuclear Information System (INIS)

    Sonino, T.

    1976-11-01

    Four methodologies for forecasting energy demand are reviewed here after considering the role of energy in the economy and the analysis of energy use in different economic sectors. The special case of Israel is considered throughout, and some forecasts for energy demands in the year 2000 are presented. An energy supply mix that may be considered feasible is proposed. (author)

  8. Forecasting electric demand of distribution system planing in rural and sparsely populated regions

    Energy Technology Data Exchange (ETDEWEB)

    Willis, H.L.; Buri, M.J. [ABB Automated Distribution Div., Raleigh, NC (United States); Finley, L.A. [Snohomish County PUD, Everett, WA (United States)

    1995-11-01

    Modern computerized distribution load forecasting methods, although accurate when applied to urban areas, give somewhat less satisfactory results when forecasting load growth in sparsely populated rural areas. This paper examines the differences between rural and urban load growth histories, identifying a major difference in the observed behavior of load growth. This difference is exploited in a new simulation forecasting algorithm. Tests show the new method is as accurate in forecasting rural load growth and as useful for analyzing DSM impacts than past methods, while requiring considerably lower computer resources and data than other simulation methods of comparable accuracy.

  9. Forecasting Croatian inbound tourism demand

    OpenAIRE

    Tica, Josip; Kožić, Ivan

    2015-01-01

    The aim of this paper is to present a forecasting model for the overnight stays of foreign tourists in Croatia. Tourism is one of the most important parts of the Croatian economy. It is particularly important in the context of the services sector. Regular and significant surpluses and the consumption of foreign guests are an important element of budget revenues, especially VAT. The ability to forecast the development of inbound tourism demand in a timely manner is crucial for both business...

  10. Long-term forecasts of regional, customer and use-specific energy demand

    International Nuclear Information System (INIS)

    Schwarz, Juerg

    1999-11-01

    In the future the Swiss electricity market will have to contend with changes stemming from market liberalization. The need for instruments to analyze and predict market shares of electricity is greater than ever; tools are also greatly needed to help managers and workers prepare for new beginnings and to reorient customers. The development and application of such an instrument are the object of the present thesis. A computer program produced within the context of this work can, based on an adapted bottom-up model, be used to analyze and predict the energy demand in the supply area of a medium-sized electric utility. Elektra Birseck Muenchenstein was included in the investigation as a representative medium-sized electric utility, and it provided the basis for a supply area. Current energy demand was depicted with a bottom-up approach and different scenarios of future development were calculated using a prognosis horizon of 30 years. For the market segmentation all consumer sectors had to be considered in detail. In addition, 'regionality', 'substitution' and 'customer proximity' factors had to be illustrated in the model, i.e. the regional development in the supply area, the substitution of energy sources -above all natural gas -and the detailed view of large, individual customers. The choice of a bottom-up approach created a demand for a large quantity of data, not all of which were available or could be produced. An additional crucial capability of the computer simulation was the comparison of assumptions and results of the prognoses. The users needed to be able to consider multiple future eventualities if they were to play out different scenarios to the end. Fulfilling these partly divergent criteria in the structural definition of the energy demand model was one of the large challenges of this work. The result of the dissertation is a differentiated prognosis instrument for the supply area of an electric utility. The structure of the suggested solution is

  11. Automation of energy demand forecasting

    Science.gov (United States)

    Siddique, Sanzad

    Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

  12. Development of a forecasting method of a region`s electric power demand. 1. Forecasting economic and social indexes; Chiikibetsu denryoku juyo yosoku shuhono kaihatsu ni tsuite. 1. Keizai shakai shihyo no yosoku

    Energy Technology Data Exchange (ETDEWEB)

    Minato, Y. [Shikoku Research Institute Inc., Kagawa (Japan); Yokoi, Y. [The University of Tokushima, Tokushima (Japan)

    1996-01-20

    This paper relates to the forecasting method of the electric power demands (kWh and kW) of a region, approached by not only time series analysis but economic and social indexes. Those indexes, based on historical statistics such as census and establishment statistics, are rearranged from an administrative division to a managerial division of the electric power company, and applied as fundamental information for forecasting the area`s kWh and also sales promotion. This method of forecasting the area`s kWh is based on the concept that area`s kWh is strongly connected with the population their lifestyle and their activity within the region. In the paper, the framework of the computational model system and forecast result are discussed. The population, number of households and their members, and number of employed persons, are all evaluated. The forecasting method of the area`s population proposed here is based on the concept that the transition of population consists of both natural growth and immigration. By estimating both factors, the future area`s population can be easily forecasted. The information of whether the population is increasing or decreasing is useful for forecasting the region`s kWh and required sales promotion. 8 refs., 8 figs., 3 tabs.

  13. Intermittent demand : Linking forecasting to inventory obsolescence

    NARCIS (Netherlands)

    Teunter, Ruud H.; Syntetos, Aris A.; Babai, M. Zied

    2011-01-01

    The standard method to forecast intermittent demand is that by Croston. This method is available in ERP-type solutions such as SAP and specialised forecasting software packages (e.g. Forecast Pro), and often applied in practice. It uses exponential smoothing to separately update the estimated demand

  14. Forecasting the Demand for Information Security Personnel

    Directory of Open Access Journals (Sweden)

    Anatoliy Alexandrovich Malyuk

    2016-06-01

    Full Text Available During the formation of information society the problem of determining the demand for IS personnel (DfISP, consisting of IS specialists and IS practitioners, is of particular relevance at present. The goal of the paper is to calculate the demand for IS specialists (DfISS. To achieve it we used the informal heuristic methods and introduced some important indicators for DfISP forecast. As a validation of the conceptual approach proposed we show how to apply it on the regional level of one country on one real-world example. All the reasoning and calculations can be narrowed down to the DfISS forecasting within one corporation or IS professionals of a specific profile.

  15. Demand forecast model based on CRM

    Science.gov (United States)

    Cai, Yuancui; Chen, Lichao

    2006-11-01

    With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

  16. Four methodologies to improve healthcare demand forecasting.

    Science.gov (United States)

    Côté, M J; Tucker, S L

    2001-05-01

    Forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. This task, which often is assumed by financial managers, first requires the compilation and examination of historical information. Although many quantitative forecasting methods exist, four common methods of forecasting are percent adjustment, 12-month moving average, trendline, and seasonalized forecast. These four methods are all based upon the organization's recent historical demand. Healthcare financial managers who want to project demand for healthcare services in their facility should understand the advantages and disadvantages of each method and then select the method that will best meet the organization's needs.

  17. Using imperfect advance demand information in forecasting

    NARCIS (Netherlands)

    Tan, T.

    2008-01-01

    In this paper, we consider the demand-forecasting problem of a make-to-stock system operating in a business-to-business environment where some customers provide information on their future orders, which are subject to changes in time, hence constituting imperfect advance demand information (ADI).

  18. A Hybrid Approach on Tourism Demand Forecasting

    Science.gov (United States)

    Nor, M. E.; Nurul, A. I. M.; Rusiman, M. S.

    2018-04-01

    Tourism has become one of the important industries that contributes to the country’s economy. Tourism demand forecasting gives valuable information to policy makers, decision makers and organizations related to tourism industry in order to make crucial decision and planning. However, it is challenging to produce an accurate forecast since economic data such as the tourism data is affected by social, economic and environmental factors. In this study, an equally-weighted hybrid method, which is a combination of Box-Jenkins and Artificial Neural Networks, was applied to forecast Malaysia’s tourism demand. The forecasting performance was assessed by taking the each individual method as a benchmark. The results showed that this hybrid approach outperformed the other two models

  19. Forecasting residential electricity demand in provincial China.

    Science.gov (United States)

    Liao, Hua; Liu, Yanan; Gao, Yixuan; Hao, Yu; Ma, Xiao-Wei; Wang, Kan

    2017-03-01

    In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016-2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016-2020, and populous provinces such as Guangdong will be the main contributors to the increments.

  20. Measuring inaccuracy in travel demand forecasting

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent

    2005-01-01

    as the basis for measurement. This paper presents the case against both objections. First, if one is interested in learning whether decisions about building transport infrastructure are based on reliable information, then it is exactly the traffic forecasted at the time of making the decision to build......Project promoters, forecasters, and managers sometimes object to two things in measuring inaccuracy in travel demand forecasting: (1)using the forecast made at the time of making the decision to build as the basis for measuring inaccuracy and (2)using traffic during the first year of operations...... that is of interest. Second, although ideally studies should take into account so-called demand ??ramp up?? over a period of years, the empirical evidence and practical considerations do not support this ideal requirement, at least not for large- N studies. Finally, the paper argues that large samples of inaccuracy...

  1. Demand forecasting and information platform in tourism

    Directory of Open Access Journals (Sweden)

    Li Yue

    2017-05-01

    Full Text Available Information asymmetry and the bullwhip effect have been serious problems in the tourism supply chain. Based on platform theory, this paper established a mathematical model to explore the inner mechanism of a platform’s influence on stakeholders’ ability to forecast demand in tourism. Results showed that the variance of stakeholders’ demand predictions with a platform was smaller than the variance without a platform, which meant that a platform would improve predictions of demand for stakeholders. The higher information-processing ability of the platform also had other effects on demand forecasting. Research on the inner logic of the platform’s influence on stakeholders has important theoretical and realistic value. This area is worthy of further study.

  2. Stochastic model of forecasting spare parts demand

    Directory of Open Access Journals (Sweden)

    Ivan S. Milojević

    2012-01-01

    hypothesis of the existence of phenomenon change trends, the next step in the methodology of forecasting is the determination of a specific growth curve that describes the regularity of the development in time. These curves of growth are obtained by the analytical representation (expression of dynamic lines. There are two basic stages in the process of expression and they are: - The choice of the type of curve the shape of which corresponds to the character of the dynamic order variation - the determination of the number of values (evaluation of the curve parameters. The most widespread method of forecasting is the trend extrapolation. The basis of the trend extrapolation is the continuing of past trends in the future. The simplicity of the trend extrapolation process, on the one hand, and the absence of other information on the other hand, are the main reasons why the trend extrapolation is used for forecasting. The trend extrapolation is founded on the following assumptions: - The phenomenon development can be presented as an evolutionary trajectory or trend, - General conditions that influenced the trend development in the past will not undergo substantial changes in the future. Spare parts demand forecasting is constantly being done in all warehouses, workshops, and at all levels. Without demand forecasting, neither planning nor decision making can be done. Demand forecasting is the input for determining the level of reserve, size of the order, ordering cycles, etc. The question that arises is the one of the reliability and accuracy of a forecast and its effects. Forecasting 'by feeling' is not to be dismissed if there is nothing better, but in this case, one must be prepared for forecasting failures that cause unnecessary accumulation of certain spare parts, and also a chronic shortage of other spare parts. All this significantly increases costs and does not provide a satisfactory supply of spare parts. The main problem of the application of this model is that each

  3. Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey

    Directory of Open Access Journals (Sweden)

    Mustafa Akpinar

    2017-06-01

    Full Text Available The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC. For this purpose, households and low-consuming commercial users’ four-year consumption data between the years of 2011–2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE for training utilizing MAPE with a univariate sliding window technique.

  4. Deep Neural Network Based Demand Side Short Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Seunghyoung Ryu

    2016-12-01

    Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

  5. Forecasting Ontario's blood supply and demand.

    Science.gov (United States)

    Drackley, Adam; Newbold, K Bruce; Paez, Antonio; Heddle, Nancy

    2012-02-01

    Given an aging population that requires increased medical care, an increasing number of deferrals from the donor pool, and a growing immigrant population that typically has lower donation rates, the purpose of this article is to forecast Ontario's blood supply and demand. We calculate age- and sex-specific donation and demand rates for blood supply based on 2008 data and project demand between 2008 and 2036 based on these rates and using population data from the Ontario Ministry of Finance. Results indicate that blood demand will outpace supply as early as 2012. For instance, while the total number of donations made by older cohorts is expected to increase in the coming years, the number of red blood cell (RBC) transfusions in the 70+ age group is forecasted grow from approximately 53% of all RBC transfusions in 2008 (209,515) in 2008 to 68% (546,996) by 2036. A series of alternate scenarios, including projections based on a 2% increase in supply per year and increased use of apheresis technology, delays supply shortfalls, but does not eliminate them without active management and/or multiple methods to increase supply and decrease demand. Predictions show that demand for blood products will outpace supply in the near future given current age- and sex-specific supply and demand rates. However, we note that the careful management of the blood supply by Canadian Blood Services, along with new medical techniques and the recruitment of new donors to the system, will remove future concerns. © 2012 American Association of Blood Banks.

  6. Electricity Demand Forecasting Using a Functional State Space Model

    OpenAIRE

    Nagbe , Komi; Cugliari , Jairo; Jacques , Julien

    2018-01-01

    In the last past years the liberalization of the electricity supply, the increase variability of electric appliances and their use, and the need to respond to the electricity demand in the real time had made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All this sources make hard electricity demand forecasting. To forecast the electr...

  7. Gas demand forecasting by a new artificial intelligent algorithm

    Science.gov (United States)

    Khatibi. B, Vahid; Khatibi, Elham

    2012-01-01

    Energy demand forecasting is a key issue for consumers and generators in all energy markets in the world. This paper presents a new forecasting algorithm for daily gas demand prediction. This algorithm combines a wavelet transform and forecasting models such as multi-layer perceptron (MLP), linear regression or GARCH. The proposed method is applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the proposed method.

  8. Modelling and forecasting Turkish residential electricity demand

    International Nuclear Information System (INIS)

    Dilaver, Zafer; Hunt, Lester C

    2011-01-01

    This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being -0.09 and -0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008. - Research highlights: → Estimated short run and long run expenditure elasticities of 0.38 and 1.57, respectively. → Estimated short run and long run price elasticities of -0.09 and -0.38, respectively. → Estimated UEDT has increasing (i.e. energy using) and decreasing (i.e. energy saving) periods. → Predicted Turkish residential electricity demand between 48 and 80 TWh in 2020.

  9. Demand Forecasting in the Smart Grid Paradigm: Features and Challenges

    Energy Technology Data Exchange (ETDEWEB)

    Khodayar, Mohammad E.; Wu, Hongyu

    2015-07-01

    Demand forecasting faces challenges that include a large volume of data, increasing number of factors that affect the demand profile, uncertainties in the generation profile of the distributed and renewable generation resources and lack of historical data. A hierarchical demand forecasting framework can incorporate the new technologies, customer behaviors and preferences, and environmental factors.

  10. Forecasting monthly peak demand of electricity in India—A critique

    International Nuclear Information System (INIS)

    Rallapalli, Srinivasa Rao; Ghosh, Sajal

    2012-01-01

    The nature of electricity differs from that of other commodities since electricity is a non-storable good and there have been significant seasonal and diurnal variations of demand. Under such condition, precise forecasting of demand for electricity should be an integral part of the planning process as this enables the policy makers to provide directions on cost-effective investment and on scheduling the operation of the existing and new power plants so that the supply of electricity can be made adequate enough to meet the future demand and its variations. Official load forecasting in India done by Central Electricity Authority (CEA) is often criticized for being overestimated due to inferior techniques used for forecasting. This paper tries to evaluate monthly peak demand forecasting performance predicted by CEA using trend method and compare it with those predicted by Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) model. It has been found that MSARIMA model outperforms CEA forecasts both in-sample static and out-of-sample dynamic forecast horizons in all five regional grids in India. For better load management and grid discipline, this study suggests employing sophisticated techniques like MSARIMA for peak load forecasting in India. - Highlights: ► This paper evaluates monthly peak demand forecasting performance by CEA. ► Compares CEA forecasts it with those predicted by MSARIMA model. ► MSARIMA model outperforms CEA forecasts in all five regional grids in India. ► Opportunity exists to improve the performance of CEA forecasts.

  11. Tourism Demand Modelling and Forecasting: A Review of Recent Research

    OpenAIRE

    Song, H; Li, G

    2008-01-01

    This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time-series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there i...

  12. An Optimization of Inventory Demand Forecasting in University Healthcare Centre

    Science.gov (United States)

    Bon, A. T.; Ng, T. K.

    2017-01-01

    Healthcare industry becomes an important field for human beings nowadays as it concerns about one’s health. With that, forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. Hence, a case study was conducted in University Health Centre to collect historical demand data of Panadol 650mg for 68 months from January 2009 until August 2014. The aim of the research is to optimize the overall inventory demand through forecasting techniques. Quantitative forecasting or time series forecasting model was used in the case study to forecast future data as a function of past data. Furthermore, the data pattern needs to be identified first before applying the forecasting techniques. Trend is the data pattern and then ten forecasting techniques are applied using Risk Simulator Software. Lastly, the best forecasting techniques will be find out with the least forecasting error. Among the ten forecasting techniques include single moving average, single exponential smoothing, double moving average, double exponential smoothing, regression, Holt-Winter’s additive, Seasonal additive, Holt-Winter’s multiplicative, seasonal multiplicative and Autoregressive Integrated Moving Average (ARIMA). According to the forecasting accuracy measurement, the best forecasting technique is regression analysis.

  13. Concerning the justiciability of demand forecasts

    International Nuclear Information System (INIS)

    Nierhaus, M.

    1977-01-01

    This subject plays at present in particular a role in the course of judicial examinations of immediately enforceable orders for the partial construction licences of nuclear power plants. The author distinguishes beween three kinds of forecast decisions: 1. Appraising forecast decisions with standards of judgment taken mainly from the fields of the art, culture, morality, religion are, according to the author, only legally verifyable to a limited extent. 2. With regard to forecast decisions not arguable, e.g. where the future behaviour of persons is concerned, the same should be applied basically. 3. In contrast to this, the following is applicable for programmatic, proceedingslike, or creative forecast decisions, in particular in economics: 'An administrative estimation privilege in a prognostic sense with the consequence that the court has to accept the forecast decision which lies within the forecast margins and which cannot be disproved, and that the court may not replace this forecast decision by its own probability judgment. In these cases, administration has the right to create its own forecast standards.' Judicial control in these cases was limited to certain substantive and procedural mistakes made by the administration in the course of forecast decision finding. (orig./HP) [de

  14. Concerning the justiciability of demand forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Nierhaus, M [Koeln Univ. (Germany, F.R.)

    1977-01-01

    This subject plays at present in particular a role in the course of judicial examinations of immediately enforceable orders for the partial construction licences of nuclear power plants. The author distinguishes beween three kinds of forecast decisions: 1. Appraising forecast decisions with standards of judgment taken mainly from the fields of the art, culture, morality, religion are, according to the author, only legally verifyable to a limited extent. 2. With regard to forecast decisions not arguable, e.g. where the future behaviour of persons is concerned, the same should be applied basically. 3. In contrast to this, the following is applicable for programmatic, proceedingslike, or creative forecast decisions, in particular in economics: 'An administrative estimation privilege in a prognostic sense with the consequence that the court has to accept the forecast decision which lies within the forecast margins and which cannot be disproved, and that the court may not replace this forecast decision by its own probability judgment. In these cases, administration has the right to create its own forecast standards.' Judicial control in these cases was limited to certain substantive and procedural mistakes made by the administration in the course of forecast decision finding.

  15. Water demand forecasting: review of soft computing methods.

    Science.gov (United States)

    Ghalehkhondabi, Iman; Ardjmand, Ehsan; Young, William A; Weckman, Gary R

    2017-07-01

    Demand forecasting plays a vital role in resource management for governments and private companies. Considering the scarcity of water and its inherent constraints, demand management and forecasting in this domain are critically important. Several soft computing techniques have been developed over the last few decades for water demand forecasting. This study focuses on soft computing methods of water consumption forecasting published between 2005 and 2015. These methods include artificial neural networks (ANNs), fuzzy and neuro-fuzzy models, support vector machines, metaheuristics, and system dynamics. Furthermore, it was discussed that while in short-term forecasting, ANNs have been superior in many cases, but it is still very difficult to pick a single method as the overall best. According to the literature, various methods and their hybrids are applied to water demand forecasting. However, it seems soft computing has a lot more to contribute to water demand forecasting. These contribution areas include, but are not limited, to various ANN architectures, unsupervised methods, deep learning, various metaheuristics, and ensemble methods. Moreover, it is found that soft computing methods are mainly used for short-term demand forecasting.

  16. Stochastic model of forecasting spare parts demand

    OpenAIRE

    Ivan S. Milojević; Rade V. Guberinić

    2012-01-01

    If demand is known for the whole planning period (complete information), then this type of demand or a supply system is deterministic. In the simplest cases, the demand per time unit is constant. If demand levels change over time following a precisely determined and pre-known principle, this type of demand is also classified as deterministic. This quality of demand is very rare. In most cases demand is the product of a process, for example TMS maintenance, whose progression cannot be predicte...

  17. Forecasting the demand for new telecommunication services

    DEFF Research Database (Denmark)

    Skouby, Knud Erik; Veiro, Bjørn

    1991-01-01

    A forecasting method that is applicable for new services, where little historical data have been recorded, is proposed. The method uses estimators based on economical, demographic and traffic data. Compared to traditional forecasting procedures that are built upon a solid historical record of dat...

  18. Optimised control and pipe burst detection by water demand forecasting

    NARCIS (Netherlands)

    Bakker, M.

    2014-01-01

    Water demand forecasting The total water demand in an area is the sum of the water demands of all individual domestic and industrial consumers in that area. These consumers behave in repetitive daily, weekly and annual patterns, and the same repetitive patterns can be observed in the drinking water

  19. Forecasting Tourist Arrivals and Supply and Demand Gap Analysis ...

    African Journals Online (AJOL)

    This paper aims to forecast the long term behavior of tourist arrivals and analyze the gap between supply and demand for the hotel/accommodation sector of the city of Addis Ababa. It also intends to provide vital information in regards to the sparse knowledge in the subject of forecasting tourist arrivals in Ethiopia.

  20. Ontario demand forecast from January 2004 to December 2013

    International Nuclear Information System (INIS)

    2003-01-01

    This document examined the demand forecast for electricity on the Independent Market Operator (IMO)-controlled grid in Ontario for the period 2004-2013. It serves as an assessment tool to determine whether existing and proposed generation and transmission facilities in the province will be sufficient to meet future electricity needs. Changes in methodology have been made to allow for an hourly peak versus the previously reported 20-minute peak value. Actual data through to the end of October 2002 was used to re-estimate energy demand. Compared to other developed countries, the outlook for the Canadian economy is optimistic. In addition, the economic forecast is better than that which formed the basis of the last ten-year forecast. Energy demand in the median growth scenario is increasing at an annual rate of 1.1 per cent rather than 0.9 per cent for the forecasted period of 2003-2012. The combination of a higher growth rate and a higher starting point results in a 2010 forecast of 168 TWh. It is expected that peak demand will grow faster than in the previous forecast. Summer peak demand averaging an annual growth of 1.3 per cent is forecasted for the period 2003-2012, with winter peak demand averaging a growth of 0.8 per cent. Under normal weather conditions, the electricity system is expected to peak in the summer of 2005 due to the continued demand for cooling load. However, under an extreme weather scenario, the system is already summer peaking. The improved economic outlook and higher starting point resulted in a higher forecast for energy. The electricity system is expected to winter peak during the first years of the forecasted period. The heating load is not expected to experience rapid growth in the next few years. 15 tabs., 14 figs

  1. Forecasting urban water demand: A meta-regression analysis.

    Science.gov (United States)

    Sebri, Maamar

    2016-12-01

    Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

  2. Ex-post evaluations of demand forecast accuracy

    DEFF Research Database (Denmark)

    Nicolaisen, Morten Skou; Driscoll, Patrick Arthur

    2014-01-01

    Travel demand forecasts play a crucial role in the preparation of decision support to policy makers in the field of transport planning. The results feed directly into impact appraisals such as cost benefit analyses and environmental impact assessments, which are mandatory for large public works...... projects in many countries. Over the last couple of decades there has been an increasing attention to the lack of demand forecast accuracy, but since data availability for comprehensive ex- post appraisals is problematic, such studies are still relatively rare. The present paper presents a review...... of the largest ex-post studies of demand forecast accuracy for transport infrastructure projects. The focus is twofold; to provide an overview of observed levels of demand forecast inaccuracy and to explore the primary explanations offered for the observed inaccuracy. Inaccuracy in the form of both bias...

  3. Electricity demand forecasting using regression, scenarios and pattern analysis

    CSIR Research Space (South Africa)

    Khuluse, S

    2009-02-01

    Full Text Available The objective of the study is to forecast national electricity demand patterns for a period of twenty years: total annual consumption and understanding seasonal effects. No constraint on the supply of electricity was assumed...

  4. Demand Forecasting in the Fashion Industry: A Review

    Directory of Open Access Journals (Sweden)

    Maria Elena Nenni

    2013-08-01

    Full Text Available Forecasting demand is a crucial issue for driving efficient operations management plans. This is especially the case in the fashion industry, where demand uncertainty, lack of historical data and seasonal trends usually coexist. Many approaches to this issue have been proposed in the literature over the past few decades. In this paper, forecasting methods are compared with the aim of linking approaches to the market features.

  5. Modeling and forecasting natural gas demand in Bangladesh

    International Nuclear Information System (INIS)

    Wadud, Zia; Dey, Himadri S.; Kabir, Md. Ashfanoor; Khan, Shahidul I.

    2011-01-01

    Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity - around 1.5 - for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country. - Highlights: → Natural gas demand is modeled using dynamic econometric methods, first of its kind in Bangladesh. → Income elasticity for aggregate natural gas demand in Bangladesh is large-around 1.5. → Demand is price insensitive, indicating too low prices and/or presence of large suppressed demand. → Demand forecasts reveal large divergence from previous estimates, which is important for planning. → Attempts to model demand for end-use sectors were successful only for the industrial sector.

  6. Modeling and forecasting natural gas demand in Bangladesh

    Energy Technology Data Exchange (ETDEWEB)

    Wadud, Zia, E-mail: ziawadud@yahoo.com [Bangladesh University of Engineering and Technology (Bangladesh); Dey, Himadri S. [University of Notre Dame (United States); Kabir, Md. Ashfanoor; Khan, Shahidul I. [Bangladesh University of Engineering and Technology (Bangladesh)

    2011-11-15

    Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity - around 1.5 - for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country. - Highlights: > Natural gas demand is modeled using dynamic econometric methods, first of its kind in Bangladesh. > Income elasticity for aggregate natural gas demand in Bangladesh is large-around 1.5. > Demand is price insensitive, indicating too low prices and/or presence of large suppressed demand. > Demand forecasts reveal large divergence from previous estimates, which is important for planning. > Attempts to model demand for end-use sectors were successful only for the industrial sector.

  7. Impact of onsite solar generation on system load demand forecast

    International Nuclear Information System (INIS)

    Kaur, Amanpreet; Pedro, Hugo T.C.; Coimbra, Carlos F.M.

    2013-01-01

    Highlights: • We showed the impact onsite solar generation on system demand load forecast. • Forecast performance degrades by 9% and 3% for 1 h and 15 min forecast horizons. • Error distribution for onsite case is best characterized as t-distribution. • Relation between error, solar penetration and solar variability is characterized. - Abstract: Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast has been used in power industry for a long time and there are several well established load forecasting models. But the performance of these models for future scenario of high renewable energy penetration is unclear. In this work, the impact of onsite solar power generation on the demand load forecast is analyzed for a community that meets between 10% and 15% of its annual power demand and 3–54% of its daily power demand from a solar power plant. Short-Term Load Forecasts (STLF) using persistence, machine learning and regression-based forecasting models are presented for two cases: (1) high solar penetration and (2) no penetration. Results show that for 1-h and 15-min forecasts the accuracy of the models drops by 9% and 3% with high solar penetration. Statistical analysis of the forecast errors demonstrate that the error distribution is best characterized as a t-distribution for the high penetration scenario. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. This work concludes that the demand forecast error distribution

  8. Research on energy supply, demand and economy forecasting in Japan

    International Nuclear Information System (INIS)

    Shiba, Tsuyoshi; Kamezaki, Hiroshi; Yuyama, Tomonori; Suzuki, Atsushi

    1999-10-01

    This project aims to do research on forecasts of energy demand structure and electricity generation cost in each power plant in Japan in the 21st century, considering constructing successful FBR scenario. During the process of doing research on forecasts of energy demand structure in Japan, documents published from organizations in inside and outside of Japan were collected. These documents include prospects of economic growth rate, forecasts of amount for energy supply and demand, the maximum amount of introducing new energy resources, CO2 regulation, and evaluation of energy best mixture. Organizations in Japan such as Economic Council and Japan Energy Economic Research Institute have provided long-term forecasts until the early 21st century. Meanwhile, organizations overseas have provided forecasts of economic structure, and demand and supply for energy in OECD and East Asia including Japan. In connection with forecasts of electricity generation cost in each power plant, views on the ultimate reserves and cost of resources are reviewed in this report. According to some views on oil reserves, making assumptions based on reserves/production ratio, the maximum length of the time that oil reserves will last is 150 years. In addition, this report provides summaries of cost and potential role of various resources, including solar energy and wind energy; and views on waste, safety, energy security-related externality cost, and the price of transferring CO2 emission right. (author)

  9. Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

    Science.gov (United States)

    Chen, Jinduan; Boccelli, Dominic L.

    2018-02-01

    Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

  10. Demand forecast of turbines in the offshore wind power industry

    DEFF Research Database (Denmark)

    Martinez-Neri, Ivan

    2014-01-01

    How important is it for a manufacturing company to be able to predict the demand of their products? How much will it lose in inventory costs due to a bad forecasting technique? And what if the product in question is composed of more than 100,000 parts and costs millions of euros a piece......? This article summarises the reasoning followed by a European manufacturer to determine the demand curve of finished offshore wind turbines and how to forecast it for the purpose of production planning....

  11. Forecasting Demand for Automotive Aftermarket Inventories

    Directory of Open Access Journals (Sweden)

    Ovidiu-Alin DOBRICAN

    2013-01-01

    Full Text Available Management decisions regarding the resource allocation in the automotive aftermarket in-volves a good understanding of it. This includes a better understanding of the participants in this market, the supply chains, specificities products and demand for these products. A useful instrument to anticipate the latter is the use of simulation methods, one of them being the Monte Carlo method, which, in this paper, is used to create various scenarios of supply.

  12. Forecasting telecommunication new service demand by analogy method and combined forecast

    Directory of Open Access Journals (Sweden)

    Lin Feng-Jenq

    2005-01-01

    Full Text Available In the modeling forecast field, we are usually faced with the more difficult problems of forecasting market demand for a new service or product. A new service or product is defined as that there is absence of historical data in this new market. We hardly use models to execute the forecasting work directly. In the Taiwan telecommunication industry, after liberalization in 1996, there are many new services opened continually. For optimal investment, it is necessary that the operators, who have been granted the concessions and licenses, forecast this new service within their planning process. Though there are some methods to solve or avoid this predicament, in this paper, we will propose one forecasting procedure that integrates the concept of analogy method and the idea of combined forecast to generate new service forecast. In view of the above, the first half of this paper describes the procedure of analogy method and the approach of combined forecast, and the second half provides the case of forecasting low-tier phone demand in Taiwan to illustrate this procedure's feasibility.

  13. Demand forecasting of electricity in Indonesia with limited historical data

    Science.gov (United States)

    Dwi Kartikasari, Mujiati; Rohmad Prayogi, Arif

    2018-03-01

    Demand forecasting of electricity is an important activity for electrical agents to know the description of electricity demand in future. Prediction of demand electricity can be done using time series models. In this paper, double moving average model, Holt’s exponential smoothing model, and grey model GM(1,1) are used to predict electricity demand in Indonesia under the condition of limited historical data. The result shows that grey model GM(1,1) has the smallest value of MAE (mean absolute error), MSE (mean squared error), and MAPE (mean absolute percentage error).

  14. Case study of forecasting uranium supply and demand

    International Nuclear Information System (INIS)

    Noritake, Kazumitsu

    1992-01-01

    PNC collects and analyzes information about uranium market trend, world uranium supply and demand, and world uranium resources potential in order to establish the strategy of uranium exploration. This paper outlines the results obtained to forecast uranium supply and demand. Our forecast indicates that 8,500 tU, accounting for one-sixth of the demand in the year 2001, must be met by uranium produced by mines to be newly developed. After 2019, demand cannot be met by the 123 mines currently in operation or expected to have gone into production by this year. The projected shortage must therefore be covered by uranium to be newly discovered. To preclude this occurrence, uranium exploration will have to be steadily continued in order to ensure future new uranium resources, to alleviate anxiety about future supply, and to prevent sharp price hikes. (author)

  15. The SEEC United Kingdom energy demand forecast (1993-2000)

    Energy Technology Data Exchange (ETDEWEB)

    Fouquet, R; Hawdon, D; Pearson, P; Robinson, C; Stevens, P

    1993-12-16

    The aims of this paper are to present the underlying determinants of fuel consumption, such as economic activity and prices, develop a series of simple yet reliable sectoral models of energy demand, which incorporate recent modelling developments; provide forecasts of energy demand and its environmental consequences; examine the effects of VAT on domestic fuel and increased competition in the electricity sector; and aid the present debate on energy markets. The paper analyses world oil prices, with a particular focus on Iraq's role, reviews energy policy in the UK and discusses SEEC's expectations about UK fuel prices in coming years and how they vary among sectors. It forecasts final user demand in the domestic, iron and steel, other industry, transport, agricultural, public administration and defence and miscellaneous sectors. The paper also examines the major changes that are underway in electricity generators' demand for fuel, and primary energy consumption and its environmental implications.

  16. Prediction of a service demand using combined forecasting approach

    Science.gov (United States)

    Zhou, Ling

    2017-08-01

    Forecasting facilitates cutting down operational and management costs while ensuring service level for a logistics service provider. Our case study here is to investigate how to forecast short-term logistic demand for a LTL carrier. Combined approach depends on several forecasting methods simultaneously, instead of a single method. It can offset the weakness of a forecasting method with the strength of another, which could improve the precision performance of prediction. Main issues of combined forecast modeling are how to select methods for combination, and how to find out weight coefficients among methods. The principles of method selection include that each method should apply to the problem of forecasting itself, also methods should differ in categorical feature as much as possible. Based on these principles, exponential smoothing, ARIMA and Neural Network are chosen to form the combined approach. Besides, least square technique is employed to settle the optimal weight coefficients among forecasting methods. Simulation results show the advantage of combined approach over the three single methods. The work done in the paper helps manager to select prediction method in practice.

  17. Survey Forecasts and Money Demand Functions: Some International Evidence

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch, Christian; Rülke, Jan

    2011-01-01

    We derive a money demand function from a dynamic macroeconomic general equilibrium model to analyze the correlations between professional economists’ forecasts of the growth rate of money supply, the inflation rate, the growth rate of real output, and the nominal interest rate. Upon estimating...... by the macroeconomic model....

  18. Planning and forecasting demand for aircraft engines airline fleet

    Directory of Open Access Journals (Sweden)

    А.Г. Кучер

    2007-03-01

    Full Text Available  The questions of air-engines supply system processes analysis on the basis of order planning and air-engine demand forecasting of airline’s air fleet with the use of imitating simulation methods are considered.

  19. Computerized early warning tool for material demand forecast

    NARCIS (Netherlands)

    Zhang, Wei

    2007-01-01

    One way to manage the material flows in supply chain is based on the purchase orders and the demand forecast for the next company in the chain. ASML in Veldhoven is a world leader in manufacturing lithography systems for the semiconductor industry. Within the company, a SAP system supports the

  20. PCBA demand forecasting using an evolving Takagi-Sugeno system

    NARCIS (Netherlands)

    van Rooijen, M.; Almeida, R.J.; Kaymak, U.

    2016-01-01

    This paper investigates the use of using an evolving fuzzy system for printed circuit board (PCBA) demand forecasting. The algorithm is based on the evolving Takagi-Sugeno (eTS) fuzzy system, which has the ability to incorporate new patterns by changing its internal structure in an on-line fashion.

  1. Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-01-01

    Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

  2. Energy demand forecasting method based on international statistical data

    International Nuclear Information System (INIS)

    Glanc, Z.; Kerner, A.

    1997-01-01

    Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs

  3. Energy demand forecasting method based on international statistical data

    Energy Technology Data Exchange (ETDEWEB)

    Glanc, Z; Kerner, A [Energy Information Centre, Warsaw (Poland)

    1997-09-01

    Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs.

  4. Regional-seasonal weather forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

    1980-08-01

    In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

  5. Short term forecasting of petroleum product demand in France

    International Nuclear Information System (INIS)

    Cadren, M.

    1998-01-01

    The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It's filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter's. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach's and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author)

  6. Multi-Locality Based Local and Symbiotic Computing for Interactively fast On-Demand Weather Forecasting for Small Regions, Short Durations, and Very High-Resolutions

    OpenAIRE

    Fjukstad, Bård

    2014-01-01

    Papers 1, 3 and 4 are not available in Munin: 1: Bård Fjukstad, Tor-Magne Stien Hagen, Daniel Stødle, Phuong Hoai Ha, John Markus Bjørndalen, and Otto Anshus: ‘Interactive Weather Simulation and Visualization on a Display Wall with Many-Core Compute Nodes’, in K. Jónasson (ed.): PARA 2010, Part I, LNCS 7133, pp. 142–151, 2012, © Springer-Verlag Berlin Heidelberg 3: Bård Fjukstad, John Markus Bjørndalen and Otto Anshus: ‘Accurate Weather Forecasting Through Locality Based Collaborative Computi...

  7. Forecasting fluid milk and cheese demands for the next decade.

    Science.gov (United States)

    Schmit, T M; Kaiser, H M

    2006-12-01

    Predictions of future market demands and farm prices for dairy products are important determinants in developing marketing strategies and farm-production planning decisions. The objective of this report was to use current aggregate forecast data, combined with existing econometric models of demand and supply, to forecast retail demands for fluid milk and cheese and the supply and price of farm milk over the next decade. In doing so, we can investigate whether projections of population and consumer food-spending patterns will extend or alter current consumption trends and examine the implications of future generic advertising strategies for dairy products. To conduct the forecast simulations and appropriately allocate the farm milk supply to various uses, we used a partial equilibrium model of the US domestic dairy sector that segmented the industry into retail, wholesale, and farm markets. Model simulation results indicated that declines in retail per capita demand would persist but at a reduced rate from years past and that retail per capita demand for cheese would continue to grow and strengthen over the next decade. These predictions rely on expected changes in the size of populations of various ages, races, and ethnicities and on existing patterns of spending on food at home and away from home. The combined effect of these forecasted changes in demand levels was reflected in annualized growth in the total farm-milk supply that was similar to growth realized during the past few years. Although we expect nominal farm milk prices to increase over the next decade, we expect real prices (relative to assumed growth in feed costs) to remain relatively stable and show no increase until the end of the forecast period. Supplemental industry model simulations also suggested that net losses in producer revenues would result if only nominal levels of generic advertising spending were maintained in forthcoming years. In fact, if real generic advertising expenditures are

  8. Modeling and forecasting of electrical power demands for capacity planning

    International Nuclear Information System (INIS)

    Al-Shobaki, S.; Mohsen, M.

    2007-01-01

    Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs

  9. Modeling and forecasting of electrical power demands for capacity planning

    Energy Technology Data Exchange (ETDEWEB)

    Al-Shobaki, S. [Hashemite Univ., Zarka (Jordan). Dept. of Industrial Engineering; Mohsen, M. [Hashemite Univ., Zarka (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs.

  10. Energy and electricity demand forecasting for nuclear power planning in developing countries

    International Nuclear Information System (INIS)

    1988-07-01

    This Guidebook is designed to be a reference document to forecast energy and electricity demand. It presents concepts and methodologies that have been developed to make an analytical approach to energy/electricity demand forecasting as part of the planning process. The Guidebook is divided into 6 main chapters: (Energy demand and development, energy demand analysis, electric load curve analysis, energy and electricity demand forecasting, energy and electricity demand forecasting tools used in various organizations, IAEA methodologies for energy and electricity demand forecasting) and 3 appendices (experience with case studies carried out by the IAEA, reference technical data, reference economic data). A bibliography and a glossary complete the Guidebook. Refs, figs and tabs

  11. MODELLING CHALLENGES TO FORECAST URBAN GOODS DEMAND FOR RAIL

    Directory of Open Access Journals (Sweden)

    Antonio COMI

    2015-12-01

    Full Text Available This paper explores the new research challenges for forecasting urban goods demand by rail. In fact, the growing interest to find urban logistics solutions for improving city sustainability and liveability, mainly due to the reduction of urban road accessibility and environmental constraints, has pushed to explore solutions alternative to the road. Multimodal urban logistics, based on the use of railway, seem an interesting alternative solution, but it remained mainly at conceptual level. Few studies have explored the factors, that push actors to find competitive such a system with respect to the road, and modelling framework for forecasting the relative demand. Therefore, paper reviews the current literature, investigates the factors involved in choosing such a mode, and finally, recalls a recent modelling framework and hence proposes some advancements that allow to point out the rail transport alternative.

  12. Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

    OpenAIRE

    Huang, Junbing; Tang, Yuee; Chen, Shuxing

    2018-01-01

    Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to ...

  13. Market-based demand forecasting promotes informed strategic financial planning.

    Science.gov (United States)

    Beech, A J

    2001-11-01

    Market-based demand forecasting is a method of estimating future demand for a healthcare organization's services by using a broad range of data that describe the nature of demand within the organization's service area. Such data include the primary and secondary service areas, the service-area populations by various demographic groupings, discharge utilization rates, market size, and market share by service line and organizationwide. Based on observable market dynamics, strategic planners can make a variety of explicit assumptions about future trends regarding these data to develop scenarios describing potential future demand. Financial planners then can evaluate each scenario to determine its potential effect on selected financial and operational measures, such as operating margin, days cash on hand, and debt-service coverage, and develop a strategic financial plan that covers a range of contingencies.

  14. Effective Heuristics for Capacitated Production Planning with Multiperiod Production and Demand with Forecast Band Refinement

    OpenAIRE

    Philip Kaminsky; Jayashankar M. Swaminathan

    2004-01-01

    In this paper we extend forecast band evolution and capacitated production modelling to the multiperiod demand case. In this model, forecasts of discrete demand for any period are modelled as bands and defined by lower and upper bounds on demand, such that future forecasts lie within the current band. We develop heuristics that utilize knowledge of demand forecast evolution to make production decisions in capacitated production planning environments. In our computational study we explore the ...

  15. State-level electricity demand forecasting model. [For 1980, 1985, 1990

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, H. D.

    1978-01-01

    This note briefly describes the Oak Ridge National Laboratory (ORNL) state-level electricity demand (SLED) forecasting model developed for the Nuclear Regulatory Commission. Specifically, the note presents (1) the special features of the model, (2) the methodology used to forecast electricity demand, and (3) forecasts of electricity demand and average price by sector for 15 states for 1980, 1985, 1990.

  16. Deterministic and heuristic models of forecasting spare parts demand

    Directory of Open Access Journals (Sweden)

    Ivan S. Milojević

    2012-04-01

    Full Text Available Knowing the demand of spare parts is the basis for successful spare parts inventory management. Inventory management has two aspects. The first one is operational management: acting according to certain models and making decisions in specific situations which could not have been foreseen or have not been encompassed by models. The second aspect is optimization of the model parameters by means of inventory management. Supply items demand (asset demand is the expression of customers' needs in units in the desired time and it is one of the most important parameters in the inventory management. The basic task of the supply system is demand fulfillment. In practice, demand is expressed through requisition or request. Given the conditions in which inventory management is considered, demand can be: - deterministic or stochastic, - stationary or nonstationary, - continuous or discrete, - satisfied or unsatisfied. The application of the maintenance concept is determined by the technological level of development of the assets being maintained. For example, it is hard to imagine that the concept of self-maintenance can be applied to assets developed and put into use 50 or 60 years ago. Even less complex concepts cannot be applied to those vehicles that only have indicators of engine temperature - those that react only when the engine is overheated. This means that the maintenance concepts that can be applied are the traditional preventive maintenance and the corrective maintenance. In order to be applied in a real system, modeling and simulation methods require a completely regulated system and that is not the case with this spare parts supply system. Therefore, this method, which also enables the model development, cannot be applied. Deterministic models of forecasting are almost exclusively related to the concept of preventive maintenance. Maintenance procedures are planned in advance, in accordance with exploitation and time resources. Since the timing

  17. Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

    Directory of Open Access Journals (Sweden)

    Junbing Huang

    2018-01-01

    Full Text Available Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985–2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.

  18. Safety stock placement in supply chains with demand forecast updates

    Directory of Open Access Journals (Sweden)

    Youssef Boulaksil

    2016-01-01

    Full Text Available Supply chains are exposed to many types of risks and it may not be obvious where to keep safety stocks in the supply chain to hedge against those risks, while maintaining a high customer service level. In this paper, we develop an approach to determine the safety stock levels in supply chain systems that face demand uncertainty. We model customer demand following the Martingale Model of Forecast Evolution (MMFE. An extensive body of literature discusses the safety stock placement problem in supply chains, but most studies assume independent and identically distributed demand. Our approach is based on a simulation study in which mathematical models are solved in a rolling horizon setting. It allows determining the safety stock levels at each stage of the supply chain. Based on a numerical study, we find that a big portion of the safety stocks should be placed downstream in the supply chain to achieve a high customer service level.

  19. Inventory model using bayesian dynamic linear model for demand forecasting

    Directory of Open Access Journals (Sweden)

    Marisol Valencia-Cárdenas

    2014-12-01

    Full Text Available An important factor of manufacturing process is the inventory management of terminated product. Constantly, industry is looking for better alternatives to establish an adequate plan of production and stored quantities, with optimal cost, getting quantities in a time horizon, which permits to define resources and logistics with anticipation, needed to distribute products on time. Total absence of historical data, required by many statistical models to forecast, demands the search for other kind of accurate techniques. This work presents an alternative that not only permits to forecast, in an adjusted way, but also, to provide optimal quantities to produce and store with an optimal cost, using Bayesian statistics. The proposal is illustrated with real data. Palabras clave: estadística bayesiana, optimización, modelo de inventarios, modelo lineal dinámico bayesiano. Keywords: Bayesian statistics, opti

  20. Short-term forecasting model for aggregated regional hydropower generation

    International Nuclear Information System (INIS)

    Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo

    2014-01-01

    Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations

  1. Expanding Regional Airport Usage to Accommodate Increased Air Traffic Demand

    Science.gov (United States)

    Russell, Carl R.

    2009-01-01

    Small regional airports present an underutilized source of capacity in the national air transportation system. This study sought to determine whether a 50 percent increase in national operations could be achieved by limiting demand growth at large hub airports and instead growing traffic levels at the surrounding regional airports. This demand scenario for future air traffic in the United States was generated and used as input to a 24-hour simulation of the national airspace system. Results of the demand generation process and metrics predicting the simulation results are presented, in addition to the actual simulation results. The demand generation process showed that sufficient runway capacity exists at regional airports to offload a significant portion of traffic from hub airports. Predictive metrics forecast a large reduction of delays at most major airports when demand is shifted. The simulation results then show that offloading hub traffic can significantly reduce nationwide delays.

  2. Forecast electricity demand in Quebec: Development plan 1993

    International Nuclear Information System (INIS)

    1992-01-01

    Demographic, economic, and energy prospects are the determining factors in estimating demand for electricity in Quebec. In average scenarios developed for 1992-2010, the Quebec population will grow 0.5%/y and the gross domestic product will increase 2.6%/y. Firm electricity sales by Hydro-Quebec will grow to 197.9 TWh by 2010, or 2.2%/y. Sales in the residential and farm sectors should grow 1.3%/y and sales in the general and institutional sectors should rise by 2.2%/y. Electricity demand in the industrial sector, rising at an estimated 2.9%/y in 1992-2010, is chiefly responsible for the anticipated growth in Hydro-Quebec's overall sales. The nonferrous smelting, refining, chemicals, and paper industries will account for ca 60% of this growth. In the municipal services and public transportation sectors, demand should grow 3.3%/y, and over half the growth forecast in this sector can be attributed to the impact that new uses of electricity are expected to have after 2005. High- and low-growth scenarios offer alternative visions of demand growth based on different but equally valid assumptions about demographic and economic growth. In terms of firm electricity sales, the high- and low-growth scenarios differ by 50 TWh in 2010. Hydro-Quebec has retained two strategic orientations that will influence growth in electricity sales: the development of industrial markets and extension of the energy-savings objective of 9.3 TWh forecast to the year 2000. Taking these two orientations into account, the growth rate for electricity sales in the average scenario would be 1.8%/y rather than 2.2%/y. 25 figs., 81 tabs

  3. Demand Forecasting: An Evaluation of DODs Accuracy Metric and Navys Procedures

    Science.gov (United States)

    2016-06-01

    dataset ci = unit cost for item i fi = demand forecast for item i 28 ai = actual demand for item i A close look at fCIMIP metric reveals a...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA MBA PROFESSIONAL REPORT DEMAND FORECASTING : AN EVALUATION OF DOD’S ACCURACY...June 2016 3. REPORT TYPE AND DATES COVERED MBA professional report 4. TITLE AND SUBTITLE DEMAND FORECASTING : AN EVALUATION OF DOD’S ACCURACY

  4. Power without manpower: Forecasting labour demand for Estonian energy sector

    International Nuclear Information System (INIS)

    Meriküll, Jaanika; Eamets, Raul; Humal, Katrin; Espenberg, Kerly

    2012-01-01

    As energy demand and prices continue to grow, oil shale might help mitigate the energy crisis—it can widely be found all over the world but so far has not been widely used. Estonia is unique in the world for producing a large majority of energy out of oil shale and has been set as an example in numerous papers covering oil shale deposits, technology etc. This paper is the first to analyse oil shale energy related workforce and provides scenario forecasts of the labour demand for the Estonian energy sector in 2010–2020. The contribution of the paper is twofold. First, the paper provides a valuable insight into oil shale energy related workforce, enabling to take into consideration the educational needs in countries where oil shale industry might be set up. Second, methodology-wise, the paper relates labour demand and supply to different scenarios of energy production capacities. The results illustrate problems related to aging of the workforce in energy production. If the existing trends continue in educational attainment in Estonia, there will be a serious shortage of high-skilled engineering and manufacturing specialists. Our method provides a simple yet reliable enough way to check for such problems early enough. - Highlights: ► This paper analyses oil shale energy related workforce and provides scenario forecasts. ► This is the first study to investigate the workforce related to oil shale energy production. ► The main workforce-related problem in the sector is ageing of the workforce. ► Workers immigrating to the sector during the Soviet times are at the retirement age. ► There will be a serious shortage of engineers for energy sector in the near future.

  5. SKU demand forecasting in the presence of promotions

    NARCIS (Netherlands)

    Gür Ali, Ö.; Sayin, S.; Woensel, van T.; Fransoo, J.C.

    2009-01-01

    Promotions and shorter life cycles make grocery sales forecasting more difficult, requiring more complicated models. We identify methods of increasing complexity and data preparation cost yielding increasing improvements in forecasting accuracy, by varying the forecasting technique, the input

  6. DeMand: A tool for evaluating and comparing device-level demand and supply forecast models

    DEFF Research Database (Denmark)

    Neupane, Bijay; Siksnys, Laurynas; Pedersen, Torben Bach

    2016-01-01

    Fine-grained device-level predictions of both shiftable and non-shiftable energy demand and supply is vital in order to take advantage of Demand Response (DR) for efficient utilization of Renewable Energy Sources. The selection of an effective device-level load forecast model is a challenging task......, mainly due to the diversity of the models and the lack of proper tools and datasets that can be used to validate them. In this paper, we introduce the DeMand system for fine-tuning, analyzing, and validating the device-level forecast models. The system offers several built-in device-level measurement...... datasets, forecast models, features, and errors measures, thus semi-automating most of the steps of the forecast model selection and validation process. This paper presents the architecture and data model of the DeMand system; and provides a use-case example on how one particular forecast model...

  7. An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

    OpenAIRE

    Syed Aziz Ur Rehman; Yanpeng Cai; Rizwan Fazal; Gordhan Das Walasai; Nayyar Hussain Mirjat

    2017-01-01

    Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fo...

  8. Aggregate electricity demand in South Africa: Conditional forecasts to 2030

    Energy Technology Data Exchange (ETDEWEB)

    Inglesi, Roula [Department of Economics, Faculty of Economic and Management Sciences, University of Pretoria, Main Campus, Pretoria 0002 (South Africa)

    2010-01-15

    In 2008, South Africa experienced a severe electricity crisis. Domestic and industrial electricity users had to suffer from black outs all over the country. It is argued that partially the reason was the lack of research on energy, locally. However, Eskom argues that the lack of capacity can only be solved by building new power plants. The objective of this study is to specify the variables that explain the electricity demand in South Africa and to forecast electricity demand by creating a model using the Engle-Granger methodology for co-integration and Error Correction models. By producing reliable results, this study will make a significant contribution that will improve the status quo of energy research in South Africa. The findings indicate that there is a long run relationship between electricity consumption and price as well as economic growth/income. The last few years in South Africa, price elasticity was rarely taken into account because of the low and decreasing prices in the past. The short-run dynamics of the system are affected by population growth, too After the energy crisis, Eskom, the national electricity supplier, is in search for substantial funding in order to build new power plants that will help with the envisaged lack of capacity that the company experienced. By using two scenarios for the future of growth, this study shows that the electricity demand will drop substantially due to the price policies agreed - until now - by Eskom and the National Energy Regulator South Africa (NERSA) that will affect the demand for some years. (author)

  9. Aggregate electricity demand in South Africa: Conditional forecasts to 2030

    International Nuclear Information System (INIS)

    Inglesi, Roula

    2010-01-01

    In 2008, South Africa experienced a severe electricity crisis. Domestic and industrial electricity users had to suffer from black outs all over the country. It is argued that partially the reason was the lack of research on energy, locally. However, Eskom argues that the lack of capacity can only be solved by building new power plants. The objective of this study is to specify the variables that explain the electricity demand in South Africa and to forecast electricity demand by creating a model using the Engle-Granger methodology for co-integration and Error Correction models. By producing reliable results, this study will make a significant contribution that will improve the status quo of energy research in South Africa. The findings indicate that there is a long run relationship between electricity consumption and price as well as economic growth/income. The last few years in South Africa, price elasticity was rarely taken into account because of the low and decreasing prices in the past. The short-run dynamics of the system are affected by population growth, too After the energy crisis, Eskom, the national electricity supplier, is in search for substantial funding in order to build new power plants that will help with the envisaged lack of capacity that the company experienced. By using two scenarios for the future of growth, this study shows that the electricity demand will drop substantially due to the price policies agreed - until now - by Eskom and the National Energy Regulator South Africa (NERSA) that will affect the demand for some years. (author)

  10. Forecasting seasonal demand : a serious limitation of Winters' forecasting procedure and the added value of product-aggregation

    NARCIS (Netherlands)

    Donselaar, van K.H.

    2003-01-01

    The well-known method for forecasting seasonal demand, Winters’ procedure, has a serious drawback: if the relative demand uncertainty increases (e.g. due to larger product assortments) or if the amount of historical demand data decreases (e.g. due to smaller product life cycles), the quality of the

  11. Natural gas demand forecast system based on the application of artificial neural networks

    International Nuclear Information System (INIS)

    Sanfeliu, J.M.; Doumanian, J.E.

    1997-01-01

    Gas Natural BAN, as a distribution gas company since 1993 in the north and west area of Buenos Aires Argentina, with 1,000,000 customers, had to develop a gas demand forecast system which should comply with the following basic requirements: Be able to do reliable forecasts with short historical information (2 years); Distinguish demands in areas of different characteristics, i.e. mainly residential, mainly industrial; Self-learning capability. To accomplish above goals, Gas Natural BAN chose in view of its own necessities, an artificial intelligence application (neural networks). 'SANDRA', the gas demand forecast system for gas distribution used by Gas Natural BAN, has the following features: Daily gas demand forecast, Hourly gas demand forecast and Breakdown of both forecast for each of the 3 basic zones in which the distribution area of Gas Natural BAN is divided. (au)

  12. Demand forecast: a case study at a meat agribusiness in west Santa Catarina

    Directory of Open Access Journals (Sweden)

    Cleunice Zanella

    2016-03-01

    Full Text Available Based on demand forecasts, companies plan production, financial and personnel scenarios, both in the long and short term. The forecasts are essential, especially for companies working with a push production system, for which there is no sale of collateral. They should therefore plan their production and financial systems with the aim of meeting the demand forecast of their products or services. Thus, this study was conducted at a meat agribusiness located in Chapecó, in the state of Santa Catarina, in order to analyze the demand forecasting methods used by the company. It is a case study with a qualitative approach. Data collection was conducted through semi-structured interviews with the operations manager, commercial manager and analysts who respond to the demand forecasts made. The main results highlight the use of both quantitative and qualitative methods, as well as indicating the importance of demand forecasts for the planning of the company

  13. Modeling and forecasting of electrical power demands for capacity planning

    International Nuclear Information System (INIS)

    Al-Shobaki, Salman; Mohsen, Mousa

    2008-01-01

    This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption

  14. Modeling and forecasting of electrical power demands for capacity planning

    Energy Technology Data Exchange (ETDEWEB)

    Al-Shobaki, Salman [Department of Industrial Engineering, Hashemite University, Zarka 13115 (Jordan); Mohsen, Mousa [Department of Mechanical Engineering, Hashemite University, Zarka 13115 (Jordan)

    2008-11-15

    This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption. (author)

  15. Overview, comparative assessment and recommendations of forecasting models for short-term water demand prediction

    CSIR Research Space (South Africa)

    Anele, AO

    2017-11-01

    Full Text Available -term water demand (STWD) forecasts. In view of this, an overview of forecasting methods for STWD prediction is presented. Based on that, a comparative assessment of the performance of alternative forecasting models from the different methods is studied. Times...

  16. A multivariate time series approach to modeling and forecasting demand in the emergency department.

    Science.gov (United States)

    Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

    2009-02-01

    The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

  17. Too Many, Too Few, or Just Right? Making Sense Of Conflicting RN Supply and Demand Forecasts.

    Science.gov (United States)

    Spetz, Joanne

    2015-01-01

    Forecasts of future supply and demand of health professionals are tools to guide policy, not a final statement about how the world will be in the future. Recent forecasts of RN supply and demand vary widely and are incredibly confusing for nurse leaders, nurse educators, and policymakers. To effectively incorporate forecasts into policy and planning, one must understand the structure of the forecasts and underlying assumptions. One should treat all forecasts cautiously, and use them as guides to policy rather than definitive future outcomes.

  18. Promotion demand forecast: A Case Study of Coca Cola Enterprise

    OpenAIRE

    Lai, Hoi-Yin Cecilia

    2007-01-01

    In this highly competitive business environment, forecasting becomes one of the hot topics. Every business organization uses forecasts for decision marking. Forecasting can help companies to determine the market strategy. It also helps in production planning and resources allocation. A good forecast can help the management team to make the best decision. Nowadays, it is important to develop a collaborative partnership within the supply chain. Coca Cola Enterprise (CCE) is working with its cus...

  19. Ethanol demand in Brazil: Regional approach

    International Nuclear Information System (INIS)

    Freitas, Luciano Charlita de; Kaneko, Shinji

    2011-01-01

    Successive studies attempting to clarify national aspects of ethanol demand have assisted policy makers and producers in defining strategies, but little information is available on the dynamic of regional ethanol markets. This study aims to analyze the characteristics of ethanol demand at the regional level taking into account the peculiarities of the developed center-south and the developing north-northeast regions. Regional ethanol demand is evaluated based on a set of market variables that include ethanol price, consumer's income, vehicle stock and prices of substitute fuels; i.e., gasoline and natural gas. A panel cointegration analysis with monthly observations from January 2003 to April 2010 is employed to estimate the long-run demand elasticity. The results reveal that the demand for ethanol in Brazil differs between regions. While in the center-south region the price elasticity for both ethanol and alternative fuels is high, consumption in the north-northeast is more sensitive to changes in the stock of the ethanol-powered fleet and income. These, among other evidences, suggest that the pattern of ethanol demand in the center-south region most closely resembles that in developed nations, while the pattern of demand in the north-northeast most closely resembles that in developing nations. - Research highlights: → Article consists of a first insight on regional demand for ethanol in Brazil. → It proposes a model with multiple fuels, i.e., hydrous ethanol, gasohol and natural gas. → Results evidence that figures for regional demand for ethanol differ amongst regions and with values reported for national demand. → Elasticities for the center-south keep similarities to patterns for fuel demand in developed nations while coefficients for the north-northeast are aligned to patterns on developing countries.

  20. Ethanol demand in Brazil: Regional approach

    Energy Technology Data Exchange (ETDEWEB)

    Freitas, Luciano Charlita de, E-mail: lucianofreitas@hiroshima-u.ac.j [Graduate School for International Development and Cooperation, Development Policy, Hiroshima University 1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8529 (Japan); Kaneko, Shinji [Graduate School for International Development and Cooperation, Development Policy, Hiroshima University 1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8529 (Japan)

    2011-05-15

    Successive studies attempting to clarify national aspects of ethanol demand have assisted policy makers and producers in defining strategies, but little information is available on the dynamic of regional ethanol markets. This study aims to analyze the characteristics of ethanol demand at the regional level taking into account the peculiarities of the developed center-south and the developing north-northeast regions. Regional ethanol demand is evaluated based on a set of market variables that include ethanol price, consumer's income, vehicle stock and prices of substitute fuels; i.e., gasoline and natural gas. A panel cointegration analysis with monthly observations from January 2003 to April 2010 is employed to estimate the long-run demand elasticity. The results reveal that the demand for ethanol in Brazil differs between regions. While in the center-south region the price elasticity for both ethanol and alternative fuels is high, consumption in the north-northeast is more sensitive to changes in the stock of the ethanol-powered fleet and income. These, among other evidences, suggest that the pattern of ethanol demand in the center-south region most closely resembles that in developed nations, while the pattern of demand in the north-northeast most closely resembles that in developing nations. - Research highlights: {yields} Article consists of a first insight on regional demand for ethanol in Brazil. {yields} It proposes a model with multiple fuels, i.e., hydrous ethanol, gasohol and natural gas. {yields} Results evidence that figures for regional demand for ethanol differ amongst regions and with values reported for national demand. {yields} Elasticities for the center-south keep similarities to patterns for fuel demand in developed nations while coefficients for the north-northeast are aligned to patterns on developing countries.

  1. Forecast analysis of the electricity supply-demand balance in France for summer 2013

    International Nuclear Information System (INIS)

    2013-05-01

    Under normal meteorological conditions, and notwithstanding localized risks associated with the vulnerability of certain regions, the forecast outlook for the electricity supply-demand balance in continental France shows no particular risk for the entire summer 2013 period. Special vigilance is maintained in the Provence-Alpes-Cote d'Azur region, given the risk of forest fires and potential outages affecting the dual 400 kV link from Toulon. This assessment is based on the assumption that forecast demand for summer 2013 will remain broadly stable as compared with summer 2012, given public economic indicators, but also that the forecast availability of the French generating fleet will increase by 1100 MW compared with summer 2012. This increased availability is based on information supplied by generators, and notably includes scheduled temporary outages of certain combined cycle gas turbines. Finally, growth in photovoltaic generation (3,700 MW of installed capacity currently in France) is continuing at a sustained pace, leading to a 700 MW increase in the mean availability rate for this generation technology as compared with summer 2012. Moreover, the substantial investments already made by RTE or currently in progress to develop its network (voltage support measures, Cotentin-Maine line, etc.) have had a very positive impact on the reliability of the power system. (authors)

  2. Medium-term electric power demand forecasting based on economic-electricity transmission model

    Science.gov (United States)

    Li, Wenfeng; Bao, Fangmin; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Mao, Yubin; Wang, Jiangbo; Liu, Junhui

    2018-06-01

    Electric demand forecasting is a basic work to ensure the safe operation of power system. Based on the theories of experimental economics and econometrics, this paper introduces Prognoz Platform 7.2 intelligent adaptive modeling platform, and constructs the economic electricity transmission model that considers the economic development scenarios and the dynamic adjustment of industrial structure to predict the region's annual electricity demand, and the accurate prediction of the whole society's electricity consumption is realized. Firstly, based on the theories of experimental economics and econometrics, this dissertation attempts to find the economic indicator variables that drive the most economical growth of electricity consumption and availability, and build an annual regional macroeconomic forecast model that takes into account the dynamic adjustment of industrial structure. Secondly, it innovatively put forward the economic electricity directed conduction theory and constructed the economic power transfer function to realize the group forecast of the primary industry + rural residents living electricity consumption, urban residents living electricity, the second industry electricity consumption, the tertiary industry electricity consumption; By comparing with the actual value of economy and electricity in Henan province in 2016, the validity of EETM model is proved, and the electricity consumption of the whole province from 2017 to 2018 is predicted finally.

  3. [Demography perspectives and forecasts of the demand for electricity].

    Science.gov (United States)

    Roy, L; Guimond, E

    1995-01-01

    "Demographic perspectives form an integral part in the development of electric load forecasts. These forecasts in turn are used to justify the addition and repair of generating facilities that will supply power in the coming decades. The goal of this article is to present how demographic perspectives are incorporated into the electric load forecasting in Quebec. The first part presents the methods, hypotheses and results of population and household projections used by Hydro-Quebec in updating its latest development plan. The second section demonstrates applications of such demographic projections for forecasting the electric load, with a focus on the residential sector." (SUMMARY IN ENG AND SPA) excerpt

  4. The energy markets to 1995 - sector demand forecasts and summary. [United Kingdom

    Energy Technology Data Exchange (ETDEWEB)

    Buckley, J

    1983-01-01

    Energy demand forecasts are often based on assumptions which are uncertain and dependent upon both political and economic factors. However, there is a need for long-term energy forecasting for the benefit of industry and commerce. CIRS (Cambridge Information and Research Services Limited) have tried to fulfill this need, based on forecasts of useful heat demand sector by sector which are then converted to heat energy supply and primary requirements. The first such forecast was produced in 1975. This 1983 updated projection examines coal, oil and gas supplies in the UK to the year 1995.

  5. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the islands of Samoa at...

  6. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at...

  7. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at...

  8. Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern...

  9. An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

    Directory of Open Access Journals (Sweden)

    Syed Aziz Ur Rehman

    2017-11-01

    Full Text Available Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan’s energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA, Holt-Winter and Long-range Energy Alternate Planning (LEAP model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector’s demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16% followed by natural gas (36.57%, electricity (16.22%, coal (7.52% and LPG (1.52% in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

  10. Summer 2011 forecast analysis. Forecast analysis of the electricity supply-demand balance in France for the summer of 2011

    International Nuclear Information System (INIS)

    2011-06-01

    Twice a year, RTE publishes a forecast study of the electricity supply and demand in continental France for the summer and winter periods. The study is based on the information supplied by electric utilities concerning the expected availability of power generation means and on statistical meteorological models. Safety margins are calculated using thousands of probabilistic scenarios combining various production and consumption situations. This report is the forecast study for the summer of 2011

  11. Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain

    Science.gov (United States)

    Mircetic, Dejan; Nikolicic, Svetlana; Maslaric, Marinko; Ralevic, Nebojsa; Debelic, Borna

    2016-11-01

    Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.

  12. Role of hybrid forecasting techniques for transportation planning of broiler meat under uncertain demand in thailand

    Directory of Open Access Journals (Sweden)

    Thoranin Sujjaviriyasup

    2014-12-01

    Full Text Available One of numerous problems experiencing in supply chain management is the demand. Most demands are appeared in terms of uncertainty. The broiler meat industry is inevitably encountering the same problem. In this research, hybrid forecasting model of ARIMA and Support Vector Machine (SVMs are developed to forecast broiler meat export. In addition, ARIMA, SVMs, and Moving Average (MA are chosen for comparing the forecasting efficiency. All the forecasting models are tested and validated using the data of Brazil’s export, Canada’s export, and Thailand’s export. The hybrid model provides accuracy of the forecasted values that are 98.71%, 97.50%, and 93.01%, respectively. In addition, the hybrid model presents the least error of all MAE, RMSE, and MAPE comparing with other forecasting models. As forecasted data are applied to transportation planning, the mean absolute percentage error (MAPE of optimal value of forecasted value and actual value is 14.53%. The hybrid forecasting model shows an ability to reduce risk of total cost of transportation when broiler meat export is forecasted by using MA(2, MA(3, ARIMA, and SVM are 50.59%, 60.18%, 68.01%, and 46.55%, respectively. The results indicate that the developed forecasting model is recommended to broiler meat industries’ supply chain decision.

  13. 39 CFR 3050.26 - Documentation of demand elasticities and volume forecasts.

    Science.gov (United States)

    2010-07-01

    ... 39 Postal Service 1 2010-07-01 2010-07-01 false Documentation of demand elasticities and volume... § 3050.26 Documentation of demand elasticities and volume forecasts. By January 20 of each year, the Postal Service shall provide econometric estimates of demand elasticity for all postal products...

  14. Demand Forecasting Methods in Accommodation Establishments: A Research with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ebru ULUCAN

    2018-05-01

    Full Text Available As it being seen in every sector, demand forecasting in tourism is been conducted with various qualitative and quantitative methods. In recent years, artificial neural network models, which have been developed as an alternative to these forecasting methods, give the nearest values in forecasting with the smallest failure percentage. This study aims to reveal that accomodation establishments can use the neural network models as an alternative while forecasting their demand. With this aim, neural network models have been tested by using the sold room values between the period of 2013-2016 of a five star hotel in Istanbul and it is found that the results acquired from the testing models are the nearest values comparing the realized figures. In the light of these results, tourism demand of the hotel for 2017 and 2018 has been forecasted.

  15. Comparison between ARIMA and DES Methods of Forecasting Population for Housing Demand in Johor

    OpenAIRE

    Alias Ahmad Rizal; Zainun Noor Yasmin; Abdul Rahman Ismail

    2016-01-01

    Forecasting accuracy is a primary criterion in selecting appropriate method of prediction. Even though there are various methods of forecasting however not all of these methods are able to predict with good accuracy. This paper presents an evaluation of two methods of population forecasting for housing demand. These methods are Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES). Both of the methods are principally adopting univariate time series analysis w...

  16. A supply chain contract with flexibility as a risk-sharing mechanism for demand forecasting

    Science.gov (United States)

    Kim, Whan-Seon

    2013-06-01

    Demand forecasting is one of the main causes of the bullwhip effect in a supply chain. As a countermeasure for demand uncertainty as well as a risk-sharing mechanism for demand forecasting in a supply chain, this article studies a bilateral contract with order quantity flexibility. Under the contract, the buyer places orders in advance for the predetermined horizons and makes minimum purchase commitments. The supplier, in return, provides the buyer with the flexibility to adjust the order quantities later, according to the most updated demand information. To conduct comparative simulations, four-echelon supply chain models, that employ the contracts and different forecasting techniques under dynamic market demands, are developed. The simulation outcomes show that demand fluctuation can be effectively absorbed by the contract scheme, which enables better inventory management and customer service. Furthermore, it has been verified that the contract scheme under study plays a role as an effective coordination mechanism in a decentralised supply chain.

  17. Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

    Science.gov (United States)

    Wu, Qi

    2010-03-01

    Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

  18. A Review of Demand Forecast for Charging Facilities of Electric Vehicles

    Science.gov (United States)

    Jiming, Han; Lingyu, Kong; Yaqi, Shen; Ying, Li; Wenting, Xiong; Hao, Wang

    2017-05-01

    The demand forecasting of charging facilities is the basis of its planning and locating, which has important role in promoting the development of electric vehicles and alleviating the energy crisis. Firstly, this paper analyzes the influence of the charging mode, the electric vehicle population and the user’s charging habits on the demand of charging facilities; Secondly, considering these factors, the recent analysis on charging and switching equipment demand forecast is divided into two methods—forecast based on electric vehicle population and user traveling behavior. Then, the article analyzes the two methods and puts forward the advantages and disadvantages. Finally, in view of the defects of current research, combined with the current situation of the development of the city and comprehensive consideration of economic, political, environmental and other factors, this paper proposes an improved demand forecasting method which has great practicability and pertinence and lays the foundation for the plan of city electric facilities.

  19. The impact of implementing a demand forecasting system into a low-income country's supply chain.

    Science.gov (United States)

    Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

    2016-07-12

    To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright

  20. Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads

    Energy Technology Data Exchange (ETDEWEB)

    Pai, Ping-Feng [Department of Information Management, National Chi Nan University, 1 University Road, Puli, Nantou 545, Taiwan (China)

    2006-09-15

    Because of the privatization of electricity in many countries, load forecasting has become one of the most crucial issues in the planning and operations of electric utilities. In addition, accurate regional load forecasting can provide the transmission and distribution operators with more information. The hybrid ellipsoidal fuzzy system was originally designed to solve control and pattern recognition problems. The main objective of this investigation is to develop a hybrid ellipsoidal fuzzy system for time series forecasting (HEFST) and apply the proposed model to forecast regional electricity loads in Taiwan. Additionally, a scaled conjugate gradient learning method is employed in the supervised learning phase of the HEFST model. Subsequently, numerical data taken from the existing literature is used to demonstrate the forecasting performance of the HEFST model. Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model. Thus, the HEFST model is a valid and promising alternative for forecasting regional electricity loads. (author)

  1. Demand forecasting: methodology used to electric power consumers for irrigation

    International Nuclear Information System (INIS)

    Gangi, R.D.; Atmann, J.L.

    1989-01-01

    The utilization of load curves on the evaluation of systems behaviour, consumers and in the owners and users brought a new subsidy for the performance of forecast techniques. This paper shows how we can use these forecasting techniques and load curves in a specify situation joined to Guaira Substation, where the predominance is rural consumers with large activities in irrigation. The main objective of this study is bring by load curve modulation and the expansion of consumer market, a optimized view of load for the future years. (C.G.C.)

  2. The application of seasonal latent variable in forecasting electricity demand as an alternative method

    International Nuclear Information System (INIS)

    Sumer, Kutluk Kagan; Goktas, Ozlem; Hepsag, Aycan

    2009-01-01

    In this study, we used ARIMA, seasonal ARIMA (SARIMA) and alternatively the regression model with seasonal latent variable in forecasting electricity demand by using data that belongs to 'Kayseri and Vicinity Electricity Joint-Stock Company' over the 1997:1-2005:12 periods. This study tries to examine the advantages of forecasting with ARIMA, SARIMA methods and with the model has seasonal latent variable to each other. The results support that ARIMA and SARIMA models are unsuccessful in forecasting electricity demand. The regression model with seasonal latent variable used in this study gives more successful results than ARIMA and SARIMA models because also this model can consider seasonal fluctuations and structural breaks

  3. Comparison between ARIMA and DES Methods of Forecasting Population for Housing Demand in Johor

    Directory of Open Access Journals (Sweden)

    Alias Ahmad Rizal

    2016-01-01

    Full Text Available Forecasting accuracy is a primary criterion in selecting appropriate method of prediction. Even though there are various methods of forecasting however not all of these methods are able to predict with good accuracy. This paper presents an evaluation of two methods of population forecasting for housing demand. These methods are Autoregressive Integrated Moving Average (ARIMA and Double Exponential Smoothing (DES. Both of the methods are principally adopting univariate time series analysis which uses past and present data for forecasting. Secondary data obtained from Department of Statistics, Malaysia was used to forecast population for housing demand in Johor. Forecasting processes had generated 14 models to each of the methods and these models where evaluated using Mean Absolute Percentage Error (MAPE. It was found that 14 of Double Exponential Smoothing models and also 14 of ARIMA models had resulted to 1.674% and 5.524% of average MAPE values respectively. Hence, the Double Exponential Smoothing method outperformed the ARIMA method by reducing 4.00 % in forecasting model population for Johor state. These findings help researchers and government agency in selecting appropriate forecasting model for housing demand.

  4. Research on industrialization of electric vehicles with its demand forecast using exponential smoothing method

    Directory of Open Access Journals (Sweden)

    Zhanglin Peng

    2015-04-01

    Full Text Available Purpose: Electric vehicles industry has gotten a rapid development in the world, especially in the developed countries, but still has a gap among different countries or regions. The advanced industrialization experiences of the EVs in the developed countries will have a great helpful for the development of EVs industrialization in the developing countries. This paper seeks to research the industrialization path & prospect of American EVs by forecasting electric vehicles demand and its proportion to the whole car sales based on the historical 37 EVs monthly sales and Cars monthly sales spanning from Dec. 2010 to Dec. 2013, and find out the key measurements to help Chinese government and automobile enterprises to promote Chinese EVs industrialization. Design/methodology: Compared with Single Exponential Smoothing method and Double Exponential Smoothing method, Triple exponential smoothing method is improved and applied in this study. Findings: The research results show that:  American EVs industry will keep a sustained growth in the next 3 months.  Price of the EVs, price of fossil oil, number of charging station, EVs technology and the government market & taxation polices have a different influence to EVs sales. So EVs manufacturers and policy-makers can adjust or reformulate some technology tactics and market measurements according to the forecast results. China can learn from American EVs polices and measurements to develop Chinese EVs industry. Originality/value: The main contribution of this paper is to use the triple exponential smoothing method to forecast the electric vehicles demand and its proportion to the whole automobile sales, and analyze the industrial development of Chinese electric vehicles by American EVs industry.

  5. Electric power demand forecasting using interval time series. A comparison between VAR and iMLP

    International Nuclear Information System (INIS)

    Garcia-Ascanio, Carolina; Mate, Carlos

    2010-01-01

    Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques. In order to forecast the monthly electric power demand per hour in Spain for 2 years, this paper presents a comparison between a new forecasting approach considering vector autoregressive (VAR) forecasting models applied to interval time series (ITS) and the iMLP, the multi-layer perceptron model adapted to interval data. In the proposed comparison, for the VAR approach two models are fitted per every hour, one composed of the centre (mid-point) and radius (half-range), and another one of the lower and upper bounds according to the interval representation assumed by the ITS in the learning set. In the case of the iMLP, only the model composed of the centre and radius is fitted. The other interval representation composed of the lower and upper bounds is obtained from the linear combination of the two. This novel approach, obtaining two bivariate models each hour, makes possible to establish, for different periods in the day, which interval representation is more accurate. Furthermore, the comparison between two different techniques adapted to interval time series allows us to determine the efficiency of these models in forecasting electric power demand. It is important to note that the iMLP technique has been selected for the comparison, as it has shown its accuracy in forecasting daily electricity price intervals. This work shows the ITS forecasting methods as a potential tool that will lead to a reduction in risk when making power system planning and operational decisions. (author)

  6. Simulation of regional day-ahead PV power forecast scenarios

    DEFF Research Database (Denmark)

    Nuno, Edgar; Koivisto, Matti Juhani; Cutululis, Nicolaos Antonio

    2017-01-01

    Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often...... at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional...... PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records....

  7. Forecasting operational demand for an urban water supply zone

    Science.gov (United States)

    Zhou, S. L.; McMahon, T. A.; Walton, A.; Lewis, J.

    2002-03-01

    A time series forecasting model of hourly water consumption 24 h in advance for an urban zone within the Melbourne (Australia) water supply system is developed. The model comprises two modules—daily and hourly. The daily module is formulated as a set of equations representing the effects of three factors on water use namely seasonality, climatic correlation, and autocorrelation. The hourly module is developed to disaggregate the estimated daily consumption into hourly consumption. The models were calibrated using hourly and daily data for a 6 year period, and independently validated over an additional seven month period. Over this latter period, the hourly forecast model accounted for 66% of the variance in the peak hourly water consumption with a standard error of 162 l/p/d.

  8. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    International Nuclear Information System (INIS)

    Zulkepli, Jafri; Abidin, Norhaslinda Zainal; Fong, Chan Hwa

    2015-01-01

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars

  9. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    Energy Technology Data Exchange (ETDEWEB)

    Zulkepli, Jafri, E-mail: zhjafri@uum.edu.my; Abidin, Norhaslinda Zainal, E-mail: nhaslinda@uum.edu.my [School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Kedah (Malaysia); Fong, Chan Hwa, E-mail: hfchan7623@yahoo.com [SWM Environment Sdn. Bhd.Level 17, Menara LGB, Taman Tun Dr. Ismail Kuala Lumpur (Malaysia)

    2015-12-11

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

  10. Forecasting demand for single-period products : A case study in the apparel industry

    NARCIS (Netherlands)

    Mostard, Julien; Teunter, Ruud; de Koster, Rene

    2011-01-01

    The problem considered is that of forecasting demand for single-period products before the period starts. We study this problem for the case of a mail order apparel company that needs to order its products pre-season. The lack of historical demand data implies that other sources of data are needed.

  11. Development and validation of a regional coupled forecasting system for S2S forecasts

    Science.gov (United States)

    Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

    2017-12-01

    Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.

  12. A critical review of IEA's oil demand forecast for China

    International Nuclear Information System (INIS)

    Nel, Willem P.; Cooper, Christopher J.

    2008-01-01

    China has a rapidly growing economy with a rapidly increasing demand for oil. The International Energy Agency (IEA) investigated possible future oil demand scenarios for China in the 2006 World Energy Outlook. The debate on whether oil supplies will be constrained in the near future, because of limited new discoveries, raises the concern that the oil industry may not be able to produce sufficient oil to meet this demand. This paper examines the historical relationship between economic growth and oil consumption in a number of countries. Logistic curve characteristics are observed in the relationship between per capita economic activity and oil consumption. This research has determined that the minimum statistical (lower-bound) annual oil consumption for developed countries is 11 barrels per capita. Despite the increase reported in total energy efficiency, no developed country has been able to reduce oil consumption below this lower limit. Indeed, the IEA projections to 2030 for the OECD countries show no reduction in oil demand on a per capita basis. If this lower limit is applied to China, it is clear that the IEA projections for China are under-estimating the growth in demand for oil. This research has determined that this under-estimation could be as high as 10 million barrels per day by 2025. If proponents of Peak Oil such as Laherrere, Campbell and Deffeyes are correct about the predicted peak in oil production before 2020 then the implications of this reassessment of China's oil demand will have profound implications for mankind

  13. Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

    Directory of Open Access Journals (Sweden)

    Mustafa Akpinar

    2016-09-01

    Full Text Available Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey’s natural gas consumption increased as well in parallel with the world‘s over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey’s Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA methods. Here, 2011–2014 monthly data were prepared and divided into two series. The first series is 2011–2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently

  14. Power plant site evaluation, electric energy demand forecasts - Douglas Point Site. Volume 3. Final report

    International Nuclear Information System (INIS)

    Wilson, J.W.

    1975-07-01

    This is part of a series of reports containing an evaluation of the proposed Douglas Point nuclear generating station site located on the Potomac River in Maryland 30 miles south of Washington, D.C. This report contains chapters on the Potomac Electric Power Company's market, forecasting future demand, modelling, a residential demand model, a nonresidential demand model, the Southern Maryland Electric Cooperative Model, short term predictive accuracy, and total system requirements

  15. Methodology of demand forecast by market analysis of electric power and load curves

    International Nuclear Information System (INIS)

    Barreiro, C.J.; Atmann, J.L.

    1989-01-01

    A methodology for demand forecast of consumer classes and their aggregation is presented. An analysis of the actual attended market can be done by appropriate measures and load curves studies. The suppositions for the future market behaviour by consumer classes (industrial, residential, commercial, others) are shown, and the actions for optimise this market are foreseen, obtained by load curves modulations. The process of future demand determination is obtained by the appropriate aggregation of this segmented demands. (C.G.C.)

  16. Modelling and Forecasting Cruise Tourism Demand to İzmir by Different Artificial Neural Network Architectures

    Directory of Open Access Journals (Sweden)

    Murat Cuhadar

    2014-03-01

    Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.

  17. Using Adaptive Neural-Fuzzy Inference Systems (ANFIS for Demand Forecasting and an Application

    Directory of Open Access Journals (Sweden)

    Onur Doğan

    2016-06-01

    Full Text Available Due to the rapid increase in global competition among organizations and companies, rational approaches in decision making have become indispensable for organizations in today’s world. Establishing a safe and robust path through uncertainties and risks depends on the decision units’ ability of using scientific methods as well as technology. Demand forecasting is known to be one of the most critical problems in organizations.  A company which supports its demand forecasting mechanism with scientific methodologies could increase its productivity and efficiency in all other functions. New methods, such as fuzzy logic and artificial neural networks are frequently being used as a decision-making mechanism in organizations and companies recently.  In this study, it is aimed to solve a critical demand forecasting problem with ANFIS. In the first phase of the study, the factors which impact demand forecasting are determined, and then a database of the model is established using these factors. It has been shown that ANFIS could be used for demand forecasting.

  18. A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

    Directory of Open Access Journals (Sweden)

    Murat Yalçıntaş

    2015-08-01

    Full Text Available The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul’s water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient technologies in households and reduction in water supply loss through investments on distribution infrastructure.

  19. Study of forecasting maximum demand of electric power

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, B.C.; Hwang, Y.J. [Korea Energy Economics Institute, Euiwang (Korea, Republic of)

    1997-08-01

    As far as the past performances of power supply and demand in Korea is concerned, one of the striking phenomena is that there have been repeated periodic surpluses and shortages of power generation facilities. Precise assumption and prediction of power demands is the basic work in establishing a supply plan and carrying out the right policy since facilities investment of the power generation industry requires a tremendous amount of capital and a long construction period. The purpose of this study is to study a model for the inference and prediction of a more precise maximum demand under these backgrounds. The non-parametric model considered in this study, paying attention to meteorological factors such as temperature and humidity, does not have a simple proportionate relationship with the maximum power demand, but affects it through mutual complicated nonlinear interaction. I used the non-parametric inference technique by introducing meteorological effects without importing any literal assumption on the interaction of temperature and humidity preliminarily. According to the analysis result, it is found that the non-parametric model that introduces the number of tropical nights which shows the continuity of the meteorological effect has better prediction power than the linear model. The non- parametric model that considers both the number of tropical nights and the number of cooling days at the same time is a model for predicting maximum demand. 7 refs., 6 figs., 9 tabs.

  20. Best practices in demand forecasting: tests of universalistic, contingency and configurational theories

    OpenAIRE

    Kalchschmidt, Matteo Giacomo Maria

    2011-01-01

    While the literature on demand forecasting has examined the best practices in the field, the interpretation and definition of best practices can be difficult due to the different perspectives that the literature has adopted. First, a universalistic perspective can be considered because some specific practices are really best regardless of the context, the forecasting problems, etc. Some other contributions have also taken a contingent approach, which states that best practices depend on the s...

  1. Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

    2010-01-01

    Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

  2. Demand, supply and fuel prices forecast to the year 2000

    International Nuclear Information System (INIS)

    1984-01-01

    This paper summarizes the Western European energy situation, and deals with specific aspects under the headings: European oil prices fall until 1987; prospects for oil recovery; transport sector holds oil demand up as oil demand loses favour in other sectors; upstream uncertainties; continued slackness of European natural gas market poses threat to oil; problems for European coal industry; dramatic growth in nuclear power; breeder reactors to play minimal role; PWRs will remain dominant. The situation in individual countries - Belgium, the Netherlands, France, Germany, United Kingdom, Italy and Spain - is analysed. (U.K.)

  3. Forecasting long-term energy demand of Croatian transport sector

    DEFF Research Database (Denmark)

    Pukšec, Tomislav; Krajačić, Goran; Lulić, Zoran

    2013-01-01

    predictions for the Croatian transport sector are presented. Special emphasis is given to different influencing mechanisms, both legal and financial. The energy demand predictions presented in this paper are based on an end-use simulation model developed and tested with Croatia as a case study. The model...

  4. A train dispatching model based on fuzzy passenger demand forecasting during holidays

    Directory of Open Access Journals (Sweden)

    Fei Dou Dou

    2013-03-01

    Full Text Available Abstract: Purpose: The train dispatching is a crucial issue in the train operation adjustment when passenger flow outbursts. During holidays, the train dispatching is to meet passenger demand to the greatest extent, and ensure safety, speediness and punctuality of the train operation. In this paper, a fuzzy passenger demand forecasting model is put up, then a train dispatching optimization model is established based on passenger demand so as to evacuate stranded passengers effectively during holidays. Design/methodology/approach: First, the complex features and regularity of passenger flow during holidays are analyzed, and then a fuzzy passenger demand forecasting model is put forward based on the fuzzy set theory and time series theory. Next, the bi-objective of the train dispatching optimization model is to minimize the total operation cost of the train dispatching and unserved passenger volume during holidays. Finally, the validity of this model is illustrated with a case concerned with the Beijing-Shanghai high-speed railway in China. Findings: The case study shows that the fuzzy passenger demand forecasting model can predict outcomes more precisely than ARIMA model. Thus train dispatching optimization plan proves that a small number of trains are able to serve unserved passengers reasonably and effectively. Originality/value: On the basis of the passenger demand predictive values, the train dispatching optimization model is established, which enables train dispatching to meet passenger demand in condition that passenger flow outbursts, so as to maximize passenger demand by offering the optimal operation plan.

  5. Huge supply/demand increases seen in oxygenate forecasts

    International Nuclear Information System (INIS)

    Rhoades, A.K.

    1992-01-01

    Industry originally projected that oxygenate supply would not be able to meet the demand created by U.S. oxygenated and reformulated gasoline mandates. This paper reports that those projections have been reserved in two recent industry reports - one from Chemical Market Associates Inc. (CMAI) and one from Pace Consultants Inc. Pace's report, by Paulo Nery and Nathan Sims, predicts gasoline and oxygenates demand, and examines the role ethanol may play in changing those values. CMAI's report estimates captive supply and demand of butylenes and oxygenates. Oxygenates are entering the domestic gasoline market this winter as a result of the 1990 U.S. Clean Air Act Amendments. Methyl tertiary butyl ether (MTBE) is the most important oxygenate, although ethanol, ethyl tertiary butyl ether (ETBE), and tertiary amyl methyl ether (TAME) are gathering market strength. Ethanol's strength is derived from President Bush's ruling granting a waiver to reformulated gasoline containing ethanol. This waiver allows ethanol blends to have a vapor pressure 1 psi higher than other types of gasoline

  6. Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

    Directory of Open Access Journals (Sweden)

    H. Sadeghi

    2016-02-01

    Full Text Available Introduction: Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municipal water applications is include a variety of water demand for domestic, public, industrial and commercial. Predicting the impact of urban water demand in better planning of water resources in arid and semiarid regions are faced with water restrictions. Materials and Methods: One of the most important factors affecting the changing technological advances in production and demand functions, we must pay special attention to the layout pattern. Technology development is concerned not only technically, but also other aspects such as personal, non-economic factors (population, geographical and social factors can be analyzed. Model examined in this study, a regression model is composed of a series of structural components over time allows changed invisible accidentally. Explanatory variables technology (both crystalline and amorphous in a model according to which the material is said to be better, but because of the lack of measured variables over time can not be entered in the template. Model examined in this study, a regression model is composed of a series of structural component invisible accidentally changed over time allows. In this study, structural time series (STSM and ARMA time series models have been used to model and estimate the water demand in Isfahan. Moreover, in order to find the efficient procedure, both models have been compared to each other. The desired data in this research include water consumption in Isfahan, water price and the monthly pay

  7. Forecasting the natural gas demand in China using a self-adapting intelligent grey model

    International Nuclear Information System (INIS)

    Zeng, Bo; Li, Chuan

    2016-01-01

    Reasonably forecasting demands of natural gas in China is of significance as it could aid Chinese government in formulating energy policies and adjusting industrial structures. To this end, a self-adapting intelligent grey prediction model is proposed in this paper. Compared with conventional grey models which have the inherent drawbacks of fixed structure and poor adaptability, the proposed new model can automatically optimize model parameters according to the real data characteristics of modeling sequence. In this study, the proposed new model, discrete grey model, even difference grey model and classical grey model were employed, respectively, to simulate China's natural gas demands during 2002–2010 and forecast demands during 2011–2014. The results show the new model has the best simulative and predictive precision. Finally, the new model is used to forecast China's natural gas demand during 2015–2020. The forecast shows the demand will grow rapidly over the next six years. Therefore, in order to maintain the balance between the supplies and the demands for the natural gas in the future, Chinese government needs to take some measures, such as importing huge amounts of natural gas from abroad, increasing the domestic yield, using more alternative energy, and reducing the industrial reliance on natural gas. - Highlights: • A self-adapting intelligent grey prediction model (SIGM) is proposed in this paper. • The SIGM has the advantage of working with exponential functions and linear functions. • The SIGM solves the drawbacks of fixed structure and poor adaptability of grey models. • The demand of natural gas in China is successfully forecasted using the SIGM model. • The study findings can help Chinese government reasonably formulate energy policies.

  8. Evaluation of Ensemble Water Supply and Demands Forecasts for Water Management in the Klamath River Basin

    Science.gov (United States)

    Broman, D.; Gangopadhyay, S.; McGuire, M.; Wood, A.; Leady, Z.; Tansey, M. K.; Nelson, K.; Dahm, K.

    2017-12-01

    The Upper Klamath River Basin in south central Oregon and north central California is home to the Klamath Irrigation Project, which is operated by the Bureau of Reclamation and provides water to around 200,000 acres of agricultural lands. The project is managed in consideration of not only water deliveries to irrigators, but also wildlife refuge water demands, biological opinion requirements for Endangered Species Act (ESA) listed fish, and Tribal Trust responsibilities. Climate change has the potential to impact water management in terms of volume and timing of water and the ability to meet multiple objectives. Current operations use a spreadsheet-based decision support tool, with water supply forecasts from the National Resources Conservation Service (NRCS) and California-Nevada River Forecast Center (CNRFC). This tool is currently limited in its ability to incorporate in ensemble forecasts, which offer the potential for improved operations by quantifying forecast uncertainty. To address these limitations, this study has worked to develop a RiverWare based water resource systems model, flexible enough to use across multiple decision time-scales, from short-term operations out to long-range planning. Systems model development has been accompanied by operational system development to handle data management and multiple modeling components. Using a set of ensemble hindcasts, this study seeks to answer several questions: A) Do a new set of ensemble streamflow forecasts have additional skill beyond what?, and allow for improved decision making under changing conditions? B) Do net irrigation water requirement forecasts developed in this project to quantify agricultural demands and reservoir evaporation forecasts provide additional benefits to decision making beyond water supply forecasts? C) What benefit do ensemble forecasts have in the context of water management decisions?

  9. A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

    Directory of Open Access Journals (Sweden)

    Francesca Gagliardi

    2017-07-01

    Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

  10. Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach

    International Nuclear Information System (INIS)

    Kucukali, Serhat; Baris, Kemal

    2010-01-01

    This paper aims to forecast Turkey's short-term gross annual electricity demand by applying fuzzy logic methodology while general information on economical, political and electricity market conditions of the country is also given. Unlike most of the other forecast models about Turkey's electricity demand, which usually uses more than one parameter, gross domestic product (GDP) based on purchasing power parity was the only parameter used in the model. Proposed model made good predictions and captured the system dynamic behavior covering the years of 1970-2014. The model yielded average absolute relative errors of 3.9%. Furthermore, the model estimates a 4.5% decrease in electricity demand of Turkey in 2009 and the electricity demand growth rates are projected to be about 4% between 2010 and 2014. It is concluded that forecasting the Turkey's short-term gross electricity demand with the country's economic performance will provide more reliable projections. Forecasting the annual electricity consumption of a country could be made by any designer with the help of the fuzzy logic procedure described in this paper. The advantage of this model lies on the ability to mimic the human thinking and reasoning.

  11. Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach

    Energy Technology Data Exchange (ETDEWEB)

    Kucukali, Serhat [Civil Engineering Department, Zonguldak Karaelmas University, Incivez 67100, Zonguldak (Turkey); Baris, Kemal [Mining Engineering Department, Zonguldak Karaelmas University, Incivez 67100, Zonguldak (Turkey)

    2010-05-15

    This paper aims to forecast Turkey's short-term gross annual electricity demand by applying fuzzy logic methodology while general information on economical, political and electricity market conditions of the country is also given. Unlike most of the other forecast models about Turkey's electricity demand, which usually uses more than one parameter, gross domestic product (GDP) based on purchasing power parity was the only parameter used in the model. Proposed model made good predictions and captured the system dynamic behavior covering the years of 1970-2014. The model yielded average absolute relative errors of 3.9%. Furthermore, the model estimates a 4.5% decrease in electricity demand of Turkey in 2009 and the electricity demand growth rates are projected to be about 4% between 2010 and 2014. It is concluded that forecasting the Turkey's short-term gross electricity demand with the country's economic performance will provide more reliable projections. Forecasting the annual electricity consumption of a country could be made by any designer with the help of the fuzzy logic procedure described in this paper. The advantage of this model lies on the ability to mimic the human thinking and reasoning. (author)

  12. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    Science.gov (United States)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  13. Energy Demand and Supply Analysis and Outlook - Energy Forecast for 2001 and Policy Issues

    Energy Technology Data Exchange (ETDEWEB)

    Na, In Gang; Ryu, Ji Chul [Korea Energy Economics Institute, Euiwang (Korea)

    2000-12-01

    The energy consumption in Korea has grown at impressive rates during the last 3 decades, along with the economic growth. The global concern about the environment issue and the restructuring in Korea energy industry has an effect on the pattern and trend of energy demand in Korea. Under the situation, this research are focusing on the analysis of energy consumption and forecast of energy demand. First of all, we analyze the trends and major characteristics of energy consumption, beginning with 1970s and up to the third quarter of 2000. In the analysis of energy consumption by energy types, we also perform qualitative analysis on the trends and characteristics of each energy types, including institutional analysis. In model section, we start with the brief description of synopsis and outline the survey on empirical models for energy demand. The econometric model used in KEEI's short-term energy forecast is outlined, followed by the result of estimations. The 2001 energy demand forecast is predicted in detail by sectors and energy types. In the year 2001, weak demand is projected to continue through the First Half, and pick up its pace of growth only in the Second Half. Projected total demand is 201.3 million TOE or 4.4% growth. In the last section, the major policy issues are summarized in three sub-sections: the restructuring in energy industry, the security of energy demand and supply, international energy cooperation including south-north energy cooperation. (author). 86 refs., 43 figs., 73 tabs.

  14. Technology and demand forecasting for carbon capture and storage technology in South Korea

    International Nuclear Information System (INIS)

    Shin, Jungwoo; Lee, Chul-Yong; Kim, Hongbum

    2016-01-01

    Among the various alternatives available to reduce greenhouse gas (GHG) emissions, carbon capture and storage (CCS) is considered to be a prospective technology that could both improve economic growth and meet GHG emission reduction targets. Despite the importance of CCS, however, studies of technology and demand forecasting for CCS are scarce. This study bridges this gap in the body of knowledge on this topic by forecasting CCS technology and demand based on an integrated model. For technology forecasting, a logistic model and patent network analysis are used to compare the competitiveness of CCS technology for selected countries. For demand forecasting, a competition diffusion model is adopted to consider competition among renewable energies and forecast demand. The results show that the number of patent applications for CCS technology will increase to 16,156 worldwide and to 4,790 in Korea by 2025. We also find that the United States has the most competitive CCS technology followed by Korea and France. Moreover, about 5 million tCO_2e of GHG will be reduced by 2040 if CCS technology is adopted in Korea after 2020. - Highlights: • Carbon capture and storage (CCS) can help mitigate climate change globally. • It can both improve economic growth and meet GHG emission reduction targets. • We forecast CCS technology and demand based on an integrated model. • The US has the most competitive CCS technology followed by Korea and France. • 5 million tCO_2e of GHG will be reduced by 2040 if CCS is adopted in Korea.

  15. Least square regression based integrated multi-parameteric demand modeling for short term load forecasting

    International Nuclear Information System (INIS)

    Halepoto, I.A.; Uqaili, M.A.

    2014-01-01

    Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintained. (author)

  16. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

    International Nuclear Information System (INIS)

    Nguyen, Hang T.; Nabney, Ian T.

    2010-01-01

    This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. (author)

  17. A multi-scale relevance vector regression approach for daily urban water demand forecasting

    Science.gov (United States)

    Bai, Yun; Wang, Pu; Li, Chuan; Xie, Jingjing; Wang, Yin

    2014-09-01

    Water is one of the most important resources for economic and social developments. Daily water demand forecasting is an effective measure for scheduling urban water facilities. This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand. The approach uses the stationary wavelet transform to decompose historical time series of daily water supplies into different scales. At each scale, the wavelet coefficients are used to train a machine-learning model using the relevance vector regression (RVR) method. The estimated coefficients of the RVR outputs for all of the scales are employed to reconstruct the forecasting result through the inverse wavelet transform. To better facilitate the MSRVR forecasting, the chaos features of the daily water supply series are analyzed to determine the input variables of the RVR model. In addition, an adaptive chaos particle swarm optimization algorithm is used to find the optimal combination of the RVR model parameters. The MSRVR approach is evaluated using real data collected from two waterworks and is compared with recently reported methods. The results show that the proposed MSRVR method can forecast daily urban water demand much more precisely in terms of the normalized root-mean-square error, correlation coefficient, and mean absolute percentage error criteria.

  18. Supply and demand forecasts for natural gas in the WCSB

    International Nuclear Information System (INIS)

    Crowfoot, C.; Laustsen, G.

    2001-01-01

    A historical review of supply of natural gas in the Western Canada Sedimentary Basin (WCSB) was presented along with export capacity versus demand and the affect of reconnection on Alberta prices. This power point presentation included several graphs and charts which showed that the decline rate per well groupings suggest the pre-1996 wells are declining at about 10 per cent and flattening. The productivity profiles of recent well additions exhibit a very steep initial decline, indicating that a basin decline of 25 per cent is apparent with an expected flattening to a decline of around 20 per cent. This presentation also included a review of WCSB natural gas drilling activity and discussed natural gas well completions by type in Western Canada and British Columbia. Pipeline capacity and throughput for 1999 was also discussed with an illustration of the North American natural gas transportation grid and a graphical illustration of gas exports and Canadian sales. tabs., figs

  19. Natural gas, NGL's and crude: supply, demand and price forecasts

    International Nuclear Information System (INIS)

    Stauft, T.L.

    2003-01-01

    This paper presents an overview of the major issues to watch in the crude oil, natural gas, and natural gas liquids (NGL) markets in North America. The presentation began with background information concerning Purvin and Gertz, an employee-owned consulting firm whose employees are chemical engineers, holders of a Master of Business Administration (MBA), or economists. They specialize in providing strategic, commercial, and technical advice to the international energy industry. A closer look at each individual market was provided, looking at demand, supply, price drivers and others. The author concluded that world oil prices continue to be influenced by a war premium. Oil prices support natural gas, as well as the possibility of a supply issue. The gas processing margins have remained strong. The unknown quantities are the weather and economic recovery. figs

  20. Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

    Science.gov (United States)

    Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

    2014-01-01

    Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

  1. Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

    Science.gov (United States)

    Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

    2010-02-01

    This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

  2. Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Antonio Candelieri

    2017-03-01

    Full Text Available This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data and individual customer water consumption (AMR data. In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.

  3. Predictive Uncertainty Estimation in Water Demand Forecasting Using the Model Conditional Processor

    Directory of Open Access Journals (Sweden)

    Amos O. Anele

    2018-04-01

    Full Text Available In a previous paper, a number of potential models for short-term water demand (STWD prediction have been analysed to find the ones with the best fit. The results obtained in Anele et al. (2017 showed that hybrid models may be considered as the accurate and appropriate forecasting models for STWD prediction. However, such best single valued forecast does not guarantee reliable and robust decisions, which can be properly obtained via model uncertainty processors (MUPs. MUPs provide an estimate of the full predictive densities and not only the single valued expected prediction. Amongst other MUPs, the purpose of this paper is to use the multi-variate version of the model conditional processor (MCP, proposed by Todini (2008, to demonstrate how the estimation of the predictive probability conditional to a number of relatively good predictive models may improve our knowledge, thus reducing the predictive uncertainty (PU when forecasting into the unknown future. Through the MCP approach, the probability distribution of the future water demand can be assessed depending on the forecast provided by one or more deterministic forecasting models. Based on an average weekly data of 168 h, the probability density of the future demand is built conditional on three models’ predictions, namely the autoregressive-moving average (ARMA, feed-forward back propagation neural network (FFBP-NN and hybrid model (i.e., combined forecast from ARMA and FFBP-NN. The results obtained show that MCP may be effectively used for real-time STWD prediction since it brings out the PU connected to its forecast, and such information could help water utilities estimate the risk connected to a decision.

  4. The (in)accuracy of travel demand forecasts in the case of no-build alternatives

    DEFF Research Database (Denmark)

    Nicolaisen, Morten Skou; Næss, Petter

    -build alternatives, in order to assess the impact of doing something rather than doing nothing. Previous research on the accuracy of demand forecasts has focused exclusively on the build alternatives, and revealed inaccuracies in the form of large imprecisions as well as systematic biases. However, little...... of dealing with congestion problems, which might prove more sustainable and resilient in the long run....

  5. Forecast demand and supply of energy in the short period. Its forecast and sensitivity analysis until the 2004 fiscal year

    International Nuclear Information System (INIS)

    Yamashita, Yukari; Suehiro, Shigeru; Yanagisawa, Akira; Imaeda, Toshiya; Komiyama, Ryouichi

    2004-01-01

    The object of this report is forecast demand and supply of energy in the 2003 and 2004 fiscal year, which correspond to a business recovery period. A macroeconomics model and an energy supply model are calculated by changing actual GNP, crude oil rate and the rerunning period of nuclear power plants. The calculation results are compared with the reference case. In the first chapter, forecast Japanese economy until the 2004 fiscal year is explained. In the second chapter, the results of energy demand and supply in the first chapter are investigated by the home supply and consumption of primary energy (the reference case) and each energy resources. The sensitivity analytical results of actual GNP, consumer price index, home supply of the primary energy, energy expenditure, sales account of electric power, city gas and fuel by five cases such as reference, increase and decrease of oil cost and increase and decrease of economic growth are investigated. The effects of fast rerunning period of nuclear power plant and atmosphere temperature on these above demands of energies are indicated in the third chapter. (S.Y.)

  6. Forecasting energy demand and CO{sub 2}-emissions from energy production in the forest industry

    Energy Technology Data Exchange (ETDEWEB)

    Malinen, H

    1998-12-31

    The purpose of this study was to develops new energy forecasting methods for the forest industry energy use. The scenarios have been the most commonly used forecasts, but they require a lot of work. The recent scenarios, which are made for the forest industry, give a wide range of results; e.g. from 27,8 TWh to 38 TWh for electricity use in 2010. There is a need for more simple and accurate methods for forecasting. The time scale for the study is from 1975 to 2010, i.e. 36 years. The basic data for the study is collected from time period 1975 - 1995. It includes the wood use, production of main product categories and energy use in the forest industry. The factors affecting energy use at both industry level and at mill level are presented. The most probable technology trends, which can have an effect on energy production and use and CO{sub 2}-emissions are studied. Recent forecasts for the forest industry energy use till the year 2010 are referred and analysed. Three alternative forecasting methods are studied more closely. These methods are (a) Regression analysis, (b) Growth curves and (c) Delphi-method. Total electricity demand, share of purchased electricity, total fuel demand and share of process-based biofuels are estimated for the time period 1996 - 2010. The results from the different methods are compared to each other and to the recent scenarios. The comparison is made for the results concerning the energy use and the usefulness of the methods in practical work. The average energy consumption given by the forecasts for electricity was 31,6 TWh and for fuel 6,2 Mtoe in 2010. The share of purchased electricity totalled 73 % and process based fuels 77 %. The figures from 1995 are 22,8 TWh, 5,5 Mtoe, 64 % and 68 % respectively. All three methods were suitable for forecasting. All the methods required less working hours and were easier to use than scenarios. The methods gave results with a smaller deviation than scenarios, e.g. with electricity use in 2010 from

  7. Forecasting energy demand and CO{sub 2}-emissions from energy production in the forest industry

    Energy Technology Data Exchange (ETDEWEB)

    Malinen, H.

    1997-12-31

    The purpose of this study was to develops new energy forecasting methods for the forest industry energy use. The scenarios have been the most commonly used forecasts, but they require a lot of work. The recent scenarios, which are made for the forest industry, give a wide range of results; e.g. from 27,8 TWh to 38 TWh for electricity use in 2010. There is a need for more simple and accurate methods for forecasting. The time scale for the study is from 1975 to 2010, i.e. 36 years. The basic data for the study is collected from time period 1975 - 1995. It includes the wood use, production of main product categories and energy use in the forest industry. The factors affecting energy use at both industry level and at mill level are presented. The most probable technology trends, which can have an effect on energy production and use and CO{sub 2}-emissions are studied. Recent forecasts for the forest industry energy use till the year 2010 are referred and analysed. Three alternative forecasting methods are studied more closely. These methods are (a) Regression analysis, (b) Growth curves and (c) Delphi-method. Total electricity demand, share of purchased electricity, total fuel demand and share of process-based biofuels are estimated for the time period 1996 - 2010. The results from the different methods are compared to each other and to the recent scenarios. The comparison is made for the results concerning the energy use and the usefulness of the methods in practical work. The average energy consumption given by the forecasts for electricity was 31,6 TWh and for fuel 6,2 Mtoe in 2010. The share of purchased electricity totalled 73 % and process based fuels 77 %. The figures from 1995 are 22,8 TWh, 5,5 Mtoe, 64 % and 68 % respectively. All three methods were suitable for forecasting. All the methods required less working hours and were easier to use than scenarios. The methods gave results with a smaller deviation than scenarios, e.g. with electricity use in 2010 from

  8. The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers

    Science.gov (United States)

    Ivanin, O. A.; Direktor, L. B.

    2018-05-01

    The problem of short-term forecasting of electric power demand of stand-alone consumers (small inhabited localities) situated outside centralized power supply areas is considered. The basic approaches to modeling the electric power demand depending on the forecasting time frame and the problems set, as well as the specific features of such modeling, are described. The advantages and disadvantages of the methods used for the short-term forecast of the electric demand are indicated, and difficulties involved in the solution of the problem are outlined. The basic principles of arranging artificial neural networks are set forth; it is also shown that the proposed method is preferable when the input information necessary for prediction is lacking or incomplete. The selection of the parameters that should be included into the list of the input data for modeling the electric power demand of residential areas using artificial neural networks is validated. The structure of a neural network is proposed for solving the problem of modeling the electric power demand of residential areas. The specific features of generation of the training dataset are outlined. The results of test modeling of daily electric demand curves for some settlements of Kamchatka and Yakutia based on known actual electric demand curves are provided. The reliability of the test modeling has been validated. A high value of the deviation of the modeled curve from the reference curve obtained in one of the four reference calculations is explained. The input data and the predicted power demand curves for the rural settlement of Kuokuiskii Nasleg are provided. The power demand curves were modeled for four characteristic days of the year, and they can be used in the future for designing a power supply system for the settlement. To enhance the accuracy of the method, a series of measures based on specific features of a neural network's functioning are proposed.

  9. Monthly electric energy demand forecasting with neural networks and Fourier series

    International Nuclear Information System (INIS)

    Gonzalez-Romera, E.; Jaramillo-Moran, M.A.; Carmona-Fernandez, D.

    2008-01-01

    Medium-term electric energy demand forecasting is a useful tool for grid maintenance planning and market research of electric energy companies. Several methods, such as ARIMA, regression or artificial intelligence, have been usually used to carry out those predictions. Some approaches include weather or economic variables, which strongly influence electric energy demand. Economic variables usually influence the general series trend, while weather provides a periodic behavior because of its seasonal nature. This work investigates the periodic behavior of the Spanish monthly electric demand series, obtained by rejecting the trend from the consumption series. A novel hybrid approach is proposed: the periodic behavior is forecasted with a Fourier series while the trend is predicted with a neural network. Satisfactory results have been obtained, with a lower than 2% MAPE, which improve those reached when only neural networks or ARIMA were used for the same purpose. (author)

  10. Entity’s Irregular Demand Scheduling of the Wholesale Electricity Market based on the Forecast of Hourly Price Ratios

    Directory of Open Access Journals (Sweden)

    O. V. Russkov

    2015-01-01

    Full Text Available The article considers a hot issue to forecast electric power demand amounts and prices for the entities of wholesale electricity market (WEM, which are in capacity of a large user with production technology requirements prevailing over hourly energy planning ones. An electric power demand of such entities is on irregular schedule. The article analyses mathematical models, currently applied to forecast demand amounts and prices. It describes limits of time-series models and fundamental ones in case of hourly forecasting an irregular demand schedule of the electricity market entity. The features of electricity trading at WEM are carefully analysed. Factors that influence on irregularity of demand schedule of the metallurgical plant are shown. The article proposes method for the qualitative forecast of market price ratios as a tool to reduce a dependence on the accuracy of forecasting an irregular schedule of demand. It describes the differences between the offered method and the similar ones considered in research studies and scholarly works. The correlation between price ratios and relaxation in the requirements for the forecast accuracy of the electric power consumption is analysed. The efficiency function of forecast method is derived. The article puts an increased focus on description of the mathematical model based on the method of qualitative forecast. It shows main model parameters and restrictions the electricity market imposes on them. The model prototype is described as a programme module. Methods to assess an effectiveness of the proposed forecast model are examined. The positive test results of the model using JSC «Volzhsky Pipe Plant» data are given. A conclusion is drawn concerning the possibility to decrease dependence on the forecast accuracy of irregular schedule of entity’s demand at WEM. The effective trading tool has been found for the entities of irregular demand schedule at WEM. The tool application allows minimizing cost

  11. Electricity demand load forecasting of the Hellenic power system using an ARMA model

    Energy Technology Data Exchange (ETDEWEB)

    Pappas, S.Sp. [ASPETE - School of Pedagogical and Technological Education Department of Electrical Engineering Educators N. Heraklion, 141 21 Athens (Greece); Ekonomou, L.; Chatzarakis, G.E.; Skafidas, P.D. [ASPETE-School of Pedagogical and Technological Education, Department of Electrical Engineering Educators, N. Heraklion, 141 21 Athens (Greece); Karampelas, P. [Hellenic American University, IT Department, 12 Kaplanon Str., 106 80 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24 100 Kalamata (Greece); Katsikas, S.K. [University of Piraeus, Department of Technology Education and Digital Systems, 150 Androutsou St., 18 532 Piraeus (Greece)

    2010-03-15

    Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts. (author)

  12. Multi-Model Prediction for Demand Forecast in Water Distribution Networks

    Directory of Open Access Journals (Sweden)

    Rodrigo Lopez Farias

    2018-03-01

    Full Text Available This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+ for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction is forecasted and the pattern mode estimated using a Nearest Neighbor (NN classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN, the statistical Autoregressive Integrated Moving Average (ARIMA, and Double Seasonal Holt-Winters (DSHW approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

  13. Deriving local demand for stumpage from estimates of regional supply and demand.

    Science.gov (United States)

    Kent P. Connaughton; Gerard A. Majerus; David H. Jackson

    1989-01-01

    The local (Forest-level or local-area) demand for stumpage can be derived from estimates of regional supply and demand. The derivation of local demand is justified when the local timber economy is similar to the regional timber economy; a simple regression of local on nonlocal prices can be used as an empirical test of similarity between local and regional economies....

  14. Regional corrections and checking the reliability of geomagnetic forecasts

    International Nuclear Information System (INIS)

    Afanas'eva, V.I.; Shevnin, A.D.

    1978-01-01

    Regional corrections of the K index mark estimate with respect to the Moskva observatory are reviewed in order to improve the short-range forecast of the geomagnetic activity and to promote it within the aqua area. The forecasts of the storms of all categories and weak perturbations have been verified for the predominant days in the catalogue of the magnetic storms family. It is shown that the adopted methods of forecasts yield considerably good results for weak perturbations as well as for weak and moderate magnetic storms. Strong and very strong storms are less predictable

  15. Water demand management in Mediterranean regions

    OpenAIRE

    Giulio Querini; Salvo Creaco

    2005-01-01

    Water sustainability needs a balance between demand and availability: 1) Water demand management: demand may be managed by suppliers and regulations responsible persons, using measures like invoicing, consumptions measurement and users education in water conservation measures; 2) Augmentation of water supply: availibility may be augmented by infrastructural measures, waste water reuse, non-conventional resources and losses reduction. Water Demand Management is about achieving a reduction in t...

  16. Pollen Dispersion Forecast At Regional Scale

    Science.gov (United States)

    Mangin, A.; Asthma Forecast System Team

    The forecast of the pollen concentration is generally based on an identification of sim- ilar coincidence of measured pollen at given points and meteorological data that is searched in an archive and which, with the help of experts, allows building a predicted value. This may be classified under the family of statistical approaches for forecast- ing. While palynologists make these methods more and more accurate with the help of innovative techniques of regression against empirical rules and/or evolving mathe- matical structures (e.g. neural networks), the spatial dispersion of the pollen is not or poorly considered, mainly because it requires a lot of means and technique that are not familiar to this scientific discipline. The research on pollen forecasts are presently mainly focused on the problematic of modeling the behavior of pollen trends and sea- sons at one location regardless of the topography, the locations of emitters, the relative strengths of emitter, in one word the Sspatial backgroundT. This research work was a & cedil;successful attempt to go a step further combining this SlocalT approach with a trans- & cedil;port/dispersion modeling allowing the access to mapping of concentration. The areas of interest that were selected for the demonstration of feasibility were 200x200km zones centered on Cordoba, Barcelona and Bologna and four pollen types were ex- amined, namely: Cupressaceae, Olea europaea, Poaceae and Parietaria. At the end of this three-year European project in December 2001, the system was fully deployed and validated. The multidisciplinary team will present the original methodologies that were derived for modeling the numerous aspects of this problem and also some con- clusions regarding potential extent to other areas and taxa.

  17. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    Science.gov (United States)

    Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

    2014-01-01

     The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall

  18. Macroeconomic Forecast in Regional Strategies: The Example of Khabarovsk Territory

    Directory of Open Access Journals (Sweden)

    Natalia Gennadyevna Zakharchenko

    2016-06-01

    Full Text Available The article discusses the methodological and instrumental problems of forecasting medium-term dynamics of a region’s socio-economic development as part of constructing the region’s socio-economic development strategy. The authors present results of constructing the system of models of different classes integrated in a single model complex. The model complex is based on the synthesis of methods of intersectoral analysis and dynamic optimization and consists of four The article was supported by the FEB RAS Program of Fundamental Research ‘Far East’ (Subprogram 8. predictive-analytical modules: a aggregate demand generation, b estimation of intersectoral interactions, c resource constraints testing d estimation of standard of living indicators. The modules are interconnected with horizontal communication channels activated within one time cycle (step or year and vertical communication channels activated between different time cycles. The authors test the model complex on the data of Khabarovsk Territory. The results of the study include quantitative assessments of macroeconomic and social parameters projections in the context of stages and scenarios of socio-economic development of the region

  19. Short-term electric power demand forecasting based on economic-electricity transmission model

    Science.gov (United States)

    Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

    2018-04-01

    Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

  20. Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine

    DEFF Research Database (Denmark)

    Mocanu, Elena; Nguyen, Phuong H.; Gibescu, Madeleine

    2016-01-01

    electric power consumption, local price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to realtime pricing signals. The results show that for the short-term (5 minute to 1 day ahead) prediction problems......The electrical demand forecasting problem can be regarded as a nonlinear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high temporal resolution. To solve this challenging problem, various time series and machine learning...... developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for electrical demand forecasting. The assessment is made on the EcoGrid dataset, originating from the Bornholm island experiment in Denmark, consisting of aggregated...

  1. Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model

    International Nuclear Information System (INIS)

    Unsihuay-Vila, C.; Zambroni de Souza, A.C.; Marangon-Lima, J.W.; Balestrassi, P.P.

    2010-01-01

    This paper proposes a new hybrid approach based on nonlinear chaotic dynamics and evolutionary strategy to forecast electricity loads and prices. The main idea is to develop a new training or identification stage in a nonlinear chaotic dynamic based predictor. In the training stage five optimal parameters for a chaotic based predictor are searched through an optimization model based on evolutionary strategy. The objective function of the optimization model is the mismatch minimization between the multi-step-ahead forecasting of predictor and observed data such as it is done in identification problems. The first contribution of this paper is that the proposed approach is capable of capturing the complex dynamic of demand and price time series considered resulting in a more accuracy forecasting. The second contribution is that the proposed approach run on-line manner, i.e. the optimal set of parameters and prediction is executed automatically which can be used to prediction in real-time, it is an advantage in comparison with other models, where the choice of their input parameters are carried out off-line, following qualitative/experience-based recipes. A case study of load and price forecasting is presented using data from New England, Alberta, and Spain. A comparison with other methods such as autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) is shown. The results show that the proposed approach provides a more accurate and effective forecasting than ARIMA and ANN methods. (author)

  2. Forecasting Monthly Electricity Demands by Wavelet Neuro-Fuzzy System Optimized by Heuristic Algorithms

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2018-02-01

    Full Text Available Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA, Cuckoo Optimization Algorithm (COA, and Cuckoo Search (CS are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA and multiple linear regression (MLR. In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.

  3. Growing an emerging energy workforce: forecasting labour demand and gaining access to emerging energy skills

    International Nuclear Information System (INIS)

    Thomsen, V.

    2006-01-01

    This paper discusses the needs of emerging energies sector in terms of growing an emerging energy workforce, forecasting labour demands and gaining access to emerging energy skills. It will require industrial renewal and innovation and not just selling our resources. It will also require educating ourselves to utilise our own finished products. Conservation is a key element in a sustainable energy future. finally, a market for renewable energy has been established in Canada

  4. Demand Forecasting for Heavy-Duty Diesel Engines Considering Emission Regulations

    Directory of Open Access Journals (Sweden)

    Yoon Seong Kim

    2017-01-01

    Full Text Available Makers of heavy-duty diesel engines (HDDEs need to reduce their inventory of old-generation products in preparation for the demand for next-generation products that satisfy new emission regulations. In this paper, a new demand forecasting model is proposed to reflect special conditions raised by the technological generational shift owing to new emission regulation enforcement. In addition, sensitivity analyses are conducted to better accommodate uncertainty involved at the time of prediction. Our proposed model can help support manufacturers’ production and sales management for a series of products in response to new emission regulations.

  5. Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

    Directory of Open Access Journals (Sweden)

    B. M. Brentan

    2017-01-01

    Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

  6. Energy Systems Scenario Modelling and Long Term Forecasting of Hourly Electricity Demand

    DEFF Research Database (Denmark)

    Alberg Østergaard, Poul; Møller Andersen, Frits; Kwon, Pil Seok

    2015-01-01

    . The results show that even with a limited short term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrate wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant...... or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model...... effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps...

  7. Forecasting the demand for health tourism in Asian countries using a GM(1,1)-Alpha model

    OpenAIRE

    Ya-Ling Huang

    2012-01-01

    The purpose – Accurately forecasting the demand for international health tourism is important to newly-emerging markets in the world. The aim of this study was presents a more suitable and accurate model for forecasting the demand for health tourism that should be more theoretically useful. Design – Applying GM(1,1) with adaptive levels of α (hereafter GM(1,1)-α model) to provide a concise prediction model that will improve the ability to forecast the demand for health tourism in Asian cou...

  8. Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China

    International Nuclear Information System (INIS)

    Wang Yuanyuan; Wang Jianzhou; Zhao Ge; Dong Yao

    2012-01-01

    Electricity demand forecasting could prove to be a useful policy tool for decision-makers; thus, accurate forecasting of electricity demand is valuable in allowing both power generators and consumers to make their plans. Although a seasonal ARIMA model is widely used in electricity demand analysis and is a high-precision approach for seasonal data forecasting, errors are unavoidable in the forecasting process. Consequently, a significant research goal is to further improve forecasting precision. To help people in the electricity sectors make more sensible decisions, this study proposes residual modification models to improve the precision of seasonal ARIMA for electricity demand forecasting. In this study, PSO optimal Fourier method, seasonal ARIMA model and combined models of PSO optimal Fourier method with seasonal ARIMA are applied in the Northwest electricity grid of China to correct the forecasting results of seasonal ARIMA. The modification models forecasting of the electricity demand appears to be more workable than that of the single seasonal ARIMA. The results indicate that the prediction accuracy of the three residual modification models is higher than the single seasonal ARIMA model and that the combined model is the most satisfactory of the three models. - Highlights: ► Three residual modification models are proposed to improve the precision of seasonal ARIMA. ► Accurate electricity demand forecast is helpful for a power production sector to come to a correct and reasonable decision. ► The results conclude that the residual modification approaches could enhance the prediction accuracy of seasonal ARIMA. ► The modification models could be applied to forecast electricity demand.

  9. Information Sharing in a Closed-Loop Supply Chain with Asymmetric Demand Forecasts

    Directory of Open Access Journals (Sweden)

    Pan Zhang

    2017-01-01

    Full Text Available This paper studies the problem of sharing demand forecast information in a closed-loop supply chain with the manufacturer collecting and remanufacturing. We investigate two scenarios: the “make-to-order” scenario, in which the manufacturer schedules production based on the realized demand, and the “make-to-stock” scenario, in which the manufacturer schedules production before the demand is known. For each scenario, we find that it is possible for the retailer to share his forecast without incentives when the collection efficiency of the manufacturer is high. When the efficiency is moderate, information sharing can be realized by a bargaining mechanism, and when the efficiency is low, non-information sharing is a unique equilibrium. Moreover, the possibility of information sharing in the make-to-stock scenario is higher than that in the make-to-order scenario. In addition, we analyze the impact of demand forecasts’ characteristics on the value of information sharing in both scenarios.

  10. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Karin Kandananond

    2011-08-01

    Full Text Available Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA, artificial neural network (ANN and multiple linear regression (MLR—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance

  11. Analysis of the demand status and forecast of food cold chain in Beijing

    Directory of Open Access Journals (Sweden)

    Hongjie Lan

    2013-03-01

    Full Text Available Purpose: Food cold chain is very important for ensuring food safety and decreasing the loss in the supply process. It is also benefit for the citizen, because cold chain could promise the food safety and the demand of the special cold food. Beijing, as the capital, the level of food chain is high, compared to other cities, and analysis of the demand status and forecast of food cold chain in Beijing is necessary, it could direct the scientific and health development of cold chain all over our country. Design/methodology/approach: In this paper, in accordance with the investigation, we analysis the demand status of food cold chain from two aspects, then according to the status, we forecast the demand of refrigerated cars and warehouse for food cold chain in Beijing with the multivariate statistics. Findings: From the analysis of the paper, we can see that the need of cold chain logistics grows rapidly, but most consumers are lack of the awareness of the importance of the cold chain and many companies cannot bear the huge investment, it make the gap of the resources of cold chain logistics large and cannot meet the normal need of cold chain logistics in Beijing. Originality/value: The result of this paper could support the relative enterprise to run business in terms of the refrigerated car and warehouse. 

  12. Analysis of PG&E`s residential end-use metered data to improve electricity demand forecasts -- final report

    Energy Technology Data Exchange (ETDEWEB)

    Eto, J.H.; Moezzi, M.M.

    1993-12-01

    This report summarizes findings from a unique project to improve the end-use electricity load shape and peak demand forecasts made by the Pacific Gas and Electric Company (PG&E) and the California Energy Commission (CEC). First, the direct incorporation of end-use metered data into electricity demand forecasting models is a new approach that has only been made possible by recent end-use metering projects. Second, and perhaps more importantly, the joint-sponsorship of this analysis has led to the development of consistent sets of forecasting model inputs. That is, the ability to use a common data base and similar data treatment conventions for some of the forecasting inputs frees forecasters to concentrate on those differences (between their competing forecasts) that stem from real differences of opinion, rather than differences that can be readily resolved with better data. The focus of the analysis is residential space cooling, which represents a large and growing demand in the PG&E service territory. Using five years of end-use metered, central air conditioner data collected by PG&E from over 300 residences, we developed consistent sets of new inputs for both PG&E`s and CEC`s end-use load shape forecasting models. We compared the performance of the new inputs both to the inputs previously used by PG&E and CEC, and to a second set of new inputs developed to take advantage of a recently added modeling option to the forecasting model. The testing criteria included ability to forecast total daily energy use, daily peak demand, and demand at 4 P.M. (the most frequent hour of PG&E`s system peak demand). We also tested the new inputs with the weather data used by PG&E and CEC in preparing their forecasts.

  13. Forecasting domestic water demand in the Haihe river basin under changing environment

    Directory of Open Access Journals (Sweden)

    X.-J. Wang

    2018-02-01

    Full Text Available A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand in the Haihe River basin under Representative Concentration Pathways (RCPs 4.5. The results showed that domestic water demand in different sub-basins of the Haihe river basin will gradually increase due to continuous increase of population and rise in temperature. It is projected to increase maximum 136.22  ×  108 m3 by GCM BNU-ESM and the minimum 107.25  ×  108 m3 by CNRM-CM5 in 2030. In spite of uncertainty in projection, it can be remarked that climate change and population growth would cause increase in water demand and consequently, reduce the gap between water supply and demand, which eventually aggravate the condition of existing water stress in the basin. Water demand management should be emphasized for adaptation to ever increasing water demand and mitigation of the impacts of environmental changes.

  14. Forecasting domestic water demand in the Haihe river basin under changing environment

    Science.gov (United States)

    Wang, Xiao-Jun; Zhang, Jian-Yun; Shahid, Shamsuddin; Xie, Yu-Xuan; Zhang, Xu

    2018-02-01

    A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs) namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand in the Haihe River basin under Representative Concentration Pathways (RCPs) 4.5. The results showed that domestic water demand in different sub-basins of the Haihe river basin will gradually increase due to continuous increase of population and rise in temperature. It is projected to increase maximum 136.22 × 108 m3 by GCM BNU-ESM and the minimum 107.25 × 108 m3 by CNRM-CM5 in 2030. In spite of uncertainty in projection, it can be remarked that climate change and population growth would cause increase in water demand and consequently, reduce the gap between water supply and demand, which eventually aggravate the condition of existing water stress in the basin. Water demand management should be emphasized for adaptation to ever increasing water demand and mitigation of the impacts of environmental changes.

  15. Improving the principles of short-term electric load forecasting of the Irkutsk region

    Directory of Open Access Journals (Sweden)

    Kornilov Vladimir

    2017-01-01

    Full Text Available Forecasting of electric load (EL is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region.

  16. Forecast of energy demand in Colombia by means of a system of inference diffuse neuronal

    International Nuclear Information System (INIS)

    Medina Hurtado, Santiago; Garcia Aguado, Josefina

    2005-01-01

    This work two artificial intelligence techniques are used lo forecast the monthly demand of electric power in Colombia, the objective is determinate the error of the prediction and they can be compared later with other traditional models of forecast time series, an important decrease in the prediction errors, would bring economic benefits for all the agents that operate in the electric market. The artificial neural networks - RNA and Adaptative Neural Fuzzy Inference Systems - ANFIS are actually broadly used in forecast problems in many fields of the science and the technology with good performance, for our case these models were fed with explanatory variables of the demand. We used a RNA totally interconnected with forward propagation and three hidden layer, two learned algorithms were proved for the net find significantly different results in the prediction error as we as in the time of training. The ANFIS model used was of type Takawi - Sugeno of order zero and it was fed with the main components of the defined entrance variables. The results were compared by means of the function of error Root of the Mean Square Error RMSE and the Percentage of Error Mean Absolute (MAPE) we find a better performance of the RNA

  17. Satellite provided customer premise services: A forecast of potential domestic demand through the year 2000. Volume 2: Technical report

    Science.gov (United States)

    Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Al-Kinani, G.

    1983-08-01

    The potential United States domestic telecommunications demand for satellite provided customer premises voice, data and video services through the year 2000 were forecast, so that this information on service demand would be available to aid in NASA program planning. To accomplish this overall purpose the following objectives were achieved: development of a forecast of the total domestic telecommunications demand, identification of that portion of the telecommunications demand suitable for transmission by satellite systems, identification of that portion of the satellite market addressable by Computer premises services systems, identification of that portion of the satellite market addressabble by Ka-band CPS system, and postulation of a Ka-band CPS network on a nationwide and local level. The approach employed included the use of a variety of forecasting models, a market distribution model and a network optimization model. Forecasts were developed for; 1980, 1990, and 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

  18. A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

    OpenAIRE

    Yalçıntaş, Murat; Bulu, Melih; Küçükvar, Murat; Samadi, Hamidreza

    2015-01-01

    Yayın, Endüstri Mühendisliği Bölümü ile ortak hazırlanmıştır; ancak tekrara düşmemek için ilk yazarın bölümü alınmıştır. The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul’s water supply and demand data is collected for the period during 2006 and 2014. Then, usi...

  19. The Impact of Implementing a Demand Forecasting System into a Low-Income Country’s Supply Chain

    Science.gov (United States)

    Mueller, Leslie E.; Haidari, Leila A.; Wateska, Angela R.; Phillips, Roslyn J.; Schmitz, Michelle M.; Connor, Diana L.; Norman, Bryan A.; Brown, Shawn T.; Welling, Joel S.; Lee, Bruce Y.

    2016-01-01

    OBJECTIVE To evaluate the potential impact and value of applications (e.g., ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country’s vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS Using our software, HERMES, we generated a detailed discrete event simulation model of Niger’s entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION Demand forecasting systems have the potential to greatly improve vaccine demand fulfillment, and decrease logistics cost/dose when implemented with storage and transportation increases direct vaccines. Simulation modeling can demonstrate the potential

  20. ECONOMETRIC FORECAST OF AGRICULTURAL SECTOR INVESTING IN LVOV REGION

    Directory of Open Access Journals (Sweden)

    Rostyslav Lytvyn

    2014-07-01

    Full Text Available Purpose of economic processes forecasting in agriculture is more relevant and urgent in recent years with application of applied econometric methods. In represented research paper, these methods are used to forecast investment and the main agricultural industry indicators of Lvov region of Ukraine. The linear trend model, the parabolic trend model and the exponential trend model were elaborated from the period from 2000 to 2009 in this scientific study using applied statistical tool STATGRAFICS and EXCEL spreadsheets. And with assistance of these models forecast for investment on the basis of data of essential indicators of agrarian sector of the region for 2010 and 2011 was made. All models with probability р=0,95 are adequate experimental data for 2000-2009 years, that allow to make the forecast of investments and main agricultural indicators of the researched region by these models for 2010 and 2011 years. Nevertheless, it should be pointed out that, because of small amount of input data analysis of regression equations coefficients have more qualitative than quantitative influence upon resulting variable y6.

  1. The relationship between energy intensity and income levels: Forecasting long term energy demand in Asian emerging countries

    International Nuclear Information System (INIS)

    Galli, R.; Univ. della Svizzera Italiana, Lugano

    1998-01-01

    This paper analyzes long-term trends in energy intensity for ten Asian emerging countries to test for a non-monotonic relationship between energy intensity and income in the author's sample. Energy demand functions are estimated during 1973--1990 using a quadratic function of log income. The long-run coefficient on squared income is found to be negative and significant, indicating a change in trend of energy intensity. The estimates are then used to evaluate a medium-term forecast of energy demand in the Asian countries, using both a log-linear and a quadratic model. It is found that in medium to high income countries the quadratic model performs better than the log-linear, with an average error of 9% against 43% in 1995. For the region as a whole, the quadratic model appears more adequate with a forecast error of 16% against 28% in 1995. These results are consistent with a process of dematerialization, which occurs as a result of a reduction of resource use per unit of GDP once an economy passes some threshold level of GDP per capita

  2. Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

    Science.gov (United States)

    Quilty, J.; Adamowski, J. F.

    2015-12-01

    Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

  3. Forecasting Air Traffic and corresponding Jet-Fuel Demand until 2025

    International Nuclear Information System (INIS)

    Cheze, Benoit; Gastineau, Pascal; Chevallier, Julien

    2010-01-01

    This paper provides i) air traffic and ii) Jet-Fuel demand projections at the worldwide level and for eight geographical zones until 2025. The general methodology may be summarized in two steps. First, air traffic forecasts are estimated using econometric methods. The modeling is performed for eight geographical zones, by using dynamic panel-data econometrics. Once estimated from historical data, the model is then used to generate air traffic forecasts. Second, the conversion of air traffic projections into quantities of Jet-Fuel is accomplished using the 'Traffic Efficiency' method developed previously by UK DTI to support the IPCC (IPCC (1999)). One of our major contribution consists in proposing an alternative methodology to obtain Energy Efficiency coefficients and energy efficiency improvements estimates based on modeling at the macro-level. These estimates are obtained by directly comparing the evolution of both Jet-Fuel consumption and air traffic time series from 1983 to 2006. According to our 'Business As Usual' scenario, air traffic should increase by about 100% between 2008 and 2025 at the world level, corresponding to a yearly average growth rate of about 4.7%. World Jet-Fuel demand is expected to increase by about 38% during the same period, corresponding to a yearly average growth rate of about 1, 9% per year. Air traffic energy efficiency improvements yield effectively to reduce the effect of air traffic rise on the Jet-Fuel demand increase, but do not annihilate it. Thus, Jet- Fuel demand is unlikely to diminish unless there is a radical technological shift, or air travel demand is restricted. (authors)

  4. Forecast analysis of the electricity supply-demand balance in France during the summer of 2008. Supply-demand balance analysis during the summer of 2008

    International Nuclear Information System (INIS)

    2008-05-01

    Twice a year, RTE publishes a forecast study of the electricity supply and demand in continental France for the summer and winter periods. The study is based on the information supplied by electric utilities concerning the expected availability of power generation means and on statistical meteorological models. Safety margins are calculated using thousands of probabilistic scenarios combining various production and consumption situations. This report is the forecast study for the summer of 2008

  5. Demand Forecast at the Foodstuff Retail Segment: a Strategic Sustainability Tool at a Small-Sized Brazilian Company

    Directory of Open Access Journals (Sweden)

    Claudimar Pereira Da Veiga

    2013-12-01

    Full Text Available Demand forecasting plays an increasingly relevant role within competitive and globalized marketplaces, in as much as operations planning and subsequent transition into a sustainable chain of supplies, is concerned. To this effect, the purpose of this study is to present the application of demand forecasting as a strategic sustainability tool at a Brazilian SME. Therefore, this is a descriptive, ex-post facto and cross-cut, sectional time case study, which employs qualitative and historical quantitative and direct observational data and that utilizes, as both indicators of the level of service offered to consumers and of opportunity costs the artificial neural networks model and fill-rates, for demand forecasting and response purposes. The study further established cause-effect relationships between prediction accuracy, demand responsiveness and process-resulting economic, environmental and social performances. Findings additionally concurred with both widely acknowledged sustainability concepts - NRBV (Natural-Resource-Based View and 3BL (Triple Bottom Line - by demonstrating that demand forecasts ensure the efficient use of resources, improvements in customer responsiveness and also mitigate supply chain stock out and overstock losses. Further to the mentioned economic benefit, demand forecasting additionally reduced the amount of waste that arises from retail product shelf-life expiration, improving the addressing of demand itself and of customer satisfaction, thus driving consequent environmental and social gains.

  6. Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

    Science.gov (United States)

    Aminyavari, Saleh; Saghafian, Bahram; Delavar, Majid

    2018-04-01

    The application of numerical weather prediction (NWP) products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous (yes/no), and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCEP, UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.

  7. Regional demand and supply projections for outdoor recreation

    Science.gov (United States)

    Donald B. K. English; Carter J. Betz; J. Mark Young; John C. Bergstrom; H. Ken Cordell

    1993-01-01

    This paper develops regional recreation supply and demand projections, by combining coefficients from the national 1989 RPA Assessment models with regional regressor values. Regional recreation opportunity estimates also are developed, based on regional travel behavior. Results show important regional variations in projections of recreation opportunities, trip supply,...

  8. The long-term forecast of Taiwan's energy supply and demand: LEAP model application

    International Nuclear Information System (INIS)

    Huang, Yophy; Bor, Yunchang Jeffrey; Peng, Chieh-Yu

    2011-01-01

    The long-term forecasting of energy supply and demand is an extremely important topic of fundamental research in Taiwan due to Taiwan's lack of natural resources, dependence on energy imports, and the nation's pursuit of sustainable development. In this article, we provide an overview of energy supply and demand in Taiwan, and a summary of the historical evolution and current status of its energy policies, as background to a description of the preparation and application of a Long-range Energy Alternatives Planning System (LEAP) model of Taiwan's energy sector. The Taiwan LEAP model is used to compare future energy demand and supply patterns, as well as greenhouse gas emissions, for several alternative scenarios of energy policy and energy sector evolution. Results of scenarios featuring 'business-as-usual' policies, aggressive energy-efficiency improvement policies, and on-schedule retirement of Taiwan's three existing nuclear plants are provided and compared, along with sensitivity cases exploring the impacts of lower economic growth assumptions. A concluding section provides an interpretation of the implications of model results for future energy and climate policies in Taiwan. - Research highlights: → The LEAP model is useful for international energy policy comparison. → Nuclear power plants have significant, positive impacts on CO 2 emission. → The most effective energy policy is to adopt demand-side management. → Reasonable energy pricing provides incentives for energy efficiency and conservation. → Financial crisis has less impact on energy demand than aggressive energy policy.

  9. Accounting for the inaccuracies in demand forecasts and construction cost estimations in transport project evaluation

    DEFF Research Database (Denmark)

    Salling, Kim Bang; Leleur, Steen

    2014-01-01

    For decades researchers have claimedthat particularly demand forecasts and construction cost estimations are assigned with/affected by a large degree of uncertainty. Massively, articles,research documents and reports agree that there exists a tendencytowards underestimating the costs...... in demand and cost estimations and hence the evaluation of transport infrastructure projects. Currently, research within this area is scarce and scattered with no commonagreement on how to embed and operationalise the huge amount of empiricaldata that exist within the frame of Optimism Bias. Therefore...... convertingdeterministic benefit-cost ratios (BCRs) into stochasticinterval results. A new data collection (2009–2013) forms the empirical basis for any risk simulation embeddedwithin the so-calledUP database (UNITE project database),revealing the inaccuracy of both construction costs and demandforecasts. Accordingly...

  10. THE ACCURACY OF DEMAND FORECAST MODELS AS A CRITICAL FACTOR IN THE FINANCIAL PERFORMANCE OF THE FOOD INDUSTRY

    Directory of Open Access Journals (Sweden)

    Cássia Rita Pereira Da Veiga

    2010-11-01

    Full Text Available Every organization needs to balance their production capacities with demand. The role of demand forecasting is to assist in the organization's strategic planning; this process allows administrators to anticipate the future and plot an appropriate course of action. On its own, however, a system of demand forecasting is not enough. It is the quality of information obtained by this system which enables the organization to achieve better operational planning. In this context, this paper presents case study research to: (a define the quantitative model to forecast demand with greater accuracy; and (b to verify the influence of accuracy in demand forecasting on financial performance. This is an ex-post facto descriptive inquiry with a time series in which we made use of historical data from five groups of products over the period 2004–2008. The results suggest that if a company employs the ARIMA model for groups A, B, and E; the Holt model for group D; and the Winter model for group C, revenues will increase by approximately $1,600,000 annually. Key-words: Accuracy. Demand forecasting. Financial performance. 

  11. Time series modelling to forecast prehospital EMS demand for diabetic emergencies.

    Science.gov (United States)

    Villani, Melanie; Earnest, Arul; Nanayakkara, Natalie; Smith, Karen; de Courten, Barbora; Zoungas, Sophia

    2017-05-05

    Acute diabetic emergencies are often managed by prehospital Emergency Medical Services (EMS). The projected growth in prevalence of diabetes is likely to result in rising demand for prehospital EMS that are already under pressure. The aims of this study were to model the temporal trends and provide forecasts of prehospital attendances for diabetic emergencies. A time series analysis on monthly cases of hypoglycemia and hyperglycemia was conducted using data from the Ambulance Victoria (AV) electronic database between 2009 and 2015. Using the seasonal autoregressive integrated moving average (SARIMA) modelling process, different models were evaluated. The most parsimonious model with the highest accuracy was selected. Forty-one thousand four hundred fifty-four prehospital diabetic emergencies were attended over a seven-year period with an increase in the annual median monthly caseload between 2009 (484.5) and 2015 (549.5). Hypoglycemia (70%) and people with type 1 diabetes (48%) accounted for most attendances. The SARIMA (0,1,0,12) model provided the best fit, with a MAPE of 4.2% and predicts a monthly caseload of approximately 740 by the end of 2017. Prehospital EMS demand for diabetic emergencies is increasing. SARIMA time series models are a valuable tool to allow forecasting of future caseload with high accuracy and predict increasing cases of prehospital diabetic emergencies into the future. The model generated by this study may be used by service providers to allow appropriate planning and resource allocation of EMS for diabetic emergencies.

  12. Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry

    Directory of Open Access Journals (Sweden)

    Shouyang Wang

    2012-03-01

    Full Text Available In this paper, petroleum product (mainly petrol and diesel consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC, establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight in aggregate (TPA, TFA, and civilian vehicle number (CVN and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015 based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA and others is made to assess our task.

  13. Medium- and long-term electric power demand forecasting based on the big data of smart city

    Science.gov (United States)

    Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie

    2017-08-01

    Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.

  14. Perturbations of modeling and forecast of karachi coastal region seawater

    International Nuclear Information System (INIS)

    Hussain, M.A.; Abbas, S.; Ansari, M.R.K.; Zaffar, A.

    2013-01-01

    Global warming is now a stark reality affecting the humanity in many hazardous ways. Continuous floods in Pakistan in past two years are an eye opener in this regard. A great loss of property, agriculture and life as a result of these floods suggests for an intelligent monitoring of the future projections of climate change and global warming. This is necessary because the harmful impacts of natural hazards can be coped and alleviated with a good planning in advance. This monitoring demands for enhanced forecasting capabilities, use of better analytical techniques and a clear determination and study of the controlling factors. Karachi is a coastal city which is also the industrial hub of Pakistan. Moreover, it is among one of the largest metropolitans of the world. So expectedly is most suitable for the study of high level of complex natural and anthropogenic activities. It is peculiar in the sense that it has two summer seasons, a situation scarcely observable on the globe. Here, summer season seawater temperature fluctuations are studied with the help of Seasonal Autoregressive Integrated Moving Average (SARIMA) models and short- and long-term forecasts are made. Our short-term forecasts determine months for the summer wise temperature extremes. It appears that the months of May, June, July and August are the months of extreme temperature for the first summer and October is the month of extreme temperature for the second summer. The long-term forecasts predict that 2014, 2016, 2018, and 2019 will be the years of warm summers. The analysis appearing here would be useful for coastal-urban planners in emphasizing the impact of seawater extreme temperatures on urban industrial activities, etc. (author)

  15. Streamlining On-Demand Access to Joint Polar Satellite System (JPSS) Data Products for Weather Forecasting

    Science.gov (United States)

    Evans, J. D.; Tislin, D.

    2017-12-01

    Observations from the Joint Polar Satellite System (JPSS) support National Weather Service (NWS) forecasters, whose Advanced Weather Interactive Processing System (AWIPS) Data Delivery (DD) will access JPSS data products on demand from the National Environmental Satellite, Data, and Information Service (NESDIS) Product Distribution and Access (PDA) service. Based on the Open Geospatial Consortium (OGC) Web Coverage Service, this on-demand service promises broad interoperability and frugal use of data networks by serving only the data that a user needs. But the volume, velocity, and variety of JPSS data products impose several challenges to such a service. It must be efficient to handle large volumes of complex, frequently updated data, and to fulfill many concurrent requests. It must offer flexible data handling and delivery, to work with a diverse and changing collection of data, and to tailor its outputs into products that users need, with minimal coordination between provider and user communities. It must support 24x7 operation, with no pauses in incoming data or user demand; and it must scale to rapid changes in data volume, variety, and demand as new satellites launch, more products come online, and users rely increasingly on the service. We are addressing these challenges in order to build an efficient and effective on-demand JPSS data service. For example, on-demand subsetting by many users at once may overload a server's processing capacity or its disk bandwidth - unless alleviated by spatial indexing, geolocation transforms, or pre-tiling and caching. Filtering by variable (/ band / layer) may also alleviate network loads, and provide fine-grained variable selection; to that end we are investigating how best to provide random access into the variety of spatiotemporal JPSS data products. Finally, producing tailored products (derivatives, aggregations) can boost flexibility for end users; but some tailoring operations may impose significant server loads

  16. Mathematic simulation of mining company’s power demand forecast (by example of “Neryungri” coal strip mine)

    Science.gov (United States)

    Antonenkov, D. V.; Solovev, D. B.

    2017-10-01

    The article covers the aspects of forecasting and consideration of the wholesale market environment in generating the power demand forecast. Major mining companies that operate in conditions of the present day power market have to provide a reliable energy demand request for a certain time period ahead, thus ensuring sufficient reduction of financial losses associated with deviations of the actual power demand from the expected figures. Normally, under the power supply agreement, the consumer is bound to provide a per-month and per-hour request annually. It means that the consumer has to generate one-month-ahead short-term and medium-term hourly forecasts. The authors discovered that empiric distributions of “Yakutugol”, Holding Joint Stock Company, power demand belong to the sustainable rank parameter H-distribution type used for generating forecasts based on extrapolation of such distribution parameters. For this reason they justify the need to apply the mathematic rank analysis in short-term forecasting of the contracted power demand of “Neryungri” coil strip mine being a component of the technocenosis-type system of the mining company “Yakutugol”, Holding JSC.

  17. Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis.

    Science.gov (United States)

    Mohammed, Emad A; Naugler, Christopher

    2017-01-01

    Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. This tool will allow anyone with historic test volume data to model future demand.

  18. Open-source software for demand forecasting of clinical laboratory test volumes using time-series analysis

    Directory of Open Access Journals (Sweden)

    Emad A Mohammed

    2017-01-01

    Full Text Available Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand.

  19. Forecasted balance sheet of the power supply and demand equilibrium in France. 2007 issue

    International Nuclear Information System (INIS)

    2007-01-01

    Conformably with the law from February 10, 2000, RTE, the French power transportation network is liable for establishing, at least every two year, a pluri-annual forecasted balance sheet of the power supply and demand equilibrium. Its aim is to identify the unbalance risks between the power consumption and the available generation means. To perform this technical expertise, RTE establishes some forecasts of domestic power consumption which are compared to the known perspectives of evolution of the production means. Two main changes have been taken into consideration in this analysis: the improvement of the energy efficiency, and the decay of power consumption in the big industry. Therefore, the new reference scenario indicates a consumption growth of 1.3% per year up to 2010 and 1% only for the next decade, i.e. 534 TWh of annual power consumption for 2020. On the offer side, several projects of new production means (mainly gas combined cycles) have been accepted during the last two years which represent more than 13000 MW of additional power. On the other hand, the decommissioning of several old fossil fuel power plants is foreseen for 2015 and represent 4400 MW. The offer based on decentralized production means is changing too, mainly thanks to the development of the wind power industry. In order to reach the supply-demand equilibrium, an acceptability threshold for failure duration has been defined by the public authorities and is limited to 3 hours per year. According to the reference scenario, the security of supplies in France seems to be reasonably assured for the next five years to come. A complement of 10500 MW will be necessary to meet the demand foreseen for 2020. (J.S.)

  20. Maximum spectral demands in the near-fault region

    Science.gov (United States)

    Huang, Yin-Nan; Whittaker, Andrew S.; Luco, Nicolas

    2008-01-01

    The Next Generation Attenuation (NGA) relationships for shallow crustal earthquakes in the western United States predict a rotated geometric mean of horizontal spectral demand, termed GMRotI50, and not maximum spectral demand. Differences between strike-normal, strike-parallel, geometric-mean, and maximum spectral demands in the near-fault region are investigated using 147 pairs of records selected from the NGA strong motion database. The selected records are for earthquakes with moment magnitude greater than 6.5 and for closest site-to-fault distance less than 15 km. Ratios of maximum spectral demand to NGA-predicted GMRotI50 for each pair of ground motions are presented. The ratio shows a clear dependence on period and the Somerville directivity parameters. Maximum demands can substantially exceed NGA-predicted GMRotI50 demands in the near-fault region, which has significant implications for seismic design, seismic performance assessment, and the next-generation seismic design maps. Strike-normal spectral demands are a significantly unconservative surrogate for maximum spectral demands for closest distance greater than 3 to 5 km. Scale factors that transform NGA-predicted GMRotI50 to a maximum spectral demand in the near-fault region are proposed.

  1. Energy systems scenario modelling and long term forecasting of hourly electricity demand

    Directory of Open Access Journals (Sweden)

    Poul Alberg Østergaard

    2015-06-01

    Full Text Available The Danish energy system is undergoing a transition from a system based on storable fossil fuels to a system based on fluctuating renewable energy sources. At the same time, more of and more of the energy system is becoming electrified; transportation, heating and fuel usage in industry and elsewhere. This article investigates the development of the Danish energy system in a medium year 2030 situation as well as in a long-term year 2050 situation. The analyses are based on scenario development by the Danish Climate Commission. In the short term, it is investigated what the effects will be of having flexible or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model. The results show that even with a limited short-term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrated wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long-term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps and electric vehicles in the long-term future overshadows any effects of changes in hourly demand curve profiles.

  2. Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ziyi Yin

    2018-03-01

    Full Text Available Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP, the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR was introduced for comparison with the ANN. The results show the following: (a The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions.

  3. Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

    Science.gov (United States)

    Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.

    2018-03-01

    Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.

  4. Forecast of energy demand in China and introduction of nuclear power using the clean development mechanism

    International Nuclear Information System (INIS)

    Ikemoto, Ichiro

    2003-01-01

    As an economic energy source with low greenhouse gas emissions and essentially no resource limitations, nuclear power is a promising option for meeting the rapidly growing energy demands of China that is being driven by rapid population and economic growth. This paper examines an introduction scenario for nuclear power in China by using the clean development mechanism, based on quantitative evaluation of energy demand forecasts and the nuclear fuel cycle through 2100. The results of the case study concluded that in the short to mid term, large-scale light water reactors will primarily be sited in coastal areas where infrastructure development is advanced. In the future, as dispersed power sources in inland areas, small scale FBRs will be preferred due to their promising safety, operation and maintenance characteristics, ease of transportation of plant equipment and plant construction and the possibility of on-site nuclear fuel cycle. Evaluation of nuclear fuel cycle showed that this introduction scenario is feasible considering natural Uranium demand, Uranium enrichment capacity and reprocessing capacity. (author)

  5. How do Dutch regional labour markets adjust to demand shocks?

    NARCIS (Netherlands)

    Broersma, Lourens; Dijk, Jouke van

    2002-01-01

    This paper analyses the response of regional labour markets in The Netherlands to region specific labour demand shocks. Whereas previous studies analyse only average patterns of all regions in a country, this paper provides also a more in debt analysis of within country differences in labour market

  6. How do Dutch regional labour markets adjust to demand shocks?

    OpenAIRE

    Broersma, Lourens; Dijk, Jouke van

    2002-01-01

    This paper analyses the response of regional labour markets in The Netherlands to region specific labour demand shocks. Whereas previous studies analyse only average patterns of all regions in a country, this paper provides also a more in debt analysis of within country differences in labour market adjustment processes. Previous studies show remarkable differences in response between regions in European countries and regions in the United States. The analysis in the present paper shows that i...

  7. Forecasting need and demand for home health care: a selective review.

    Science.gov (United States)

    Sharma, R K

    1980-01-01

    THREE MODELS FOR FORECASTING HOME HEALTH CARE (HHC) NEEDS ARE ANALYZED: HSA/SP model (Health Systems Agency of Southwestern Pennsylvania); Florida model (Florida State Department of Health and Rehabilitative Services); and Rhode Island model (Rhode Island Department of Community Affairs). A utilization approach to forecasting is also presented.In the HSA/SP and Florida models, need for HHC is based on a certain proportion of (a) hospital admissions and (b) patients entering HHC from other sources. The major advantage of these models is that they are relatively easy to use and explain; their major weaknesses are an imprecise definition of need and an incomplete model specification.The Rhode Island approach defines need for HHC in terms of the health status of the population as measured by chronic activity limitations. The major strengths of this approach are its explicit assumptions and its emphasis on consumer needs. The major drawback is that it requires considerable local area data.The utilization approach is based on extrapolation from observed utilization experience of the target population. Its main limitation is that it is based on current market imperfections; its major advantage is that it exposes existing deficiencies in HHC.The author concludes that each approach should be tested empirically in order to refine it, and that need and demand approaches be used jointly in the planning process.

  8. Forecasting international tourism demand from the US, Japan and South Korea to Malaysia: A SARIMA approach

    Science.gov (United States)

    Borhan, Nurbaizura; Arsad, Zainudin

    2014-07-01

    One of the major contributing sectors for Malaysia's economic growth is tourism. The number of international tourist arrivals to Malaysia has been showing an upward trend as a result of several programs and promotion introduced by the Malaysian government to attract international tourists to the country. This study attempts to model and to forecast tourism demand for Malaysia by three selected countries: the US, Japan and South Korea. This study utilized monthly time series data for the period from January 1999 to December 2012 and employed the well-known Box-Jenkins seasonal ARIMA modeling procedures. Not surprisingly the results show the number of tourist arrivals from the three countries contain strong seasonal component as the arrivals strongly dependent on the season in the country of origin. The findings of the study also show that the number of tourist arrivals from the US and South Korea will continue to increase in the near future. Meanwhile the arrivals from Japan is forecasted to show a drop in the near future and as such tourism authorities in Malaysia need to enhance the promotional effort to attract more tourists from Japan to visit Malaysia.

  9. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Hawaiian islands of Oahu,...

  10. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Main Hawaiian Islands (MHI)...

  11. How (In)accurate are demand forecasts in public works projects?

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Skamris, Mette; Buhl, Søren Ladegaard

    2005-01-01

    by forecasters. The causes of inaccuracy in forecasts are different for rail and road projects, with deliberately slanted forecasts playing a larger role for rail than for road. The cure is transparency, accountability, and new forecasting methods. The challenge is to change the governance structures...

  12. Regionalization of post-processed ensemble runoff forecasts

    Directory of Open Access Journals (Sweden)

    J. O. Skøien

    2016-05-01

    Full Text Available For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather, and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather. The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS (Gneiting et al., 2005. Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu, where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014. We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

  13. Study on Triopoly Dynamic Game Model Based on Different Demand Forecast Methods in the Market

    Directory of Open Access Journals (Sweden)

    Junhai Ma

    2017-01-01

    Full Text Available The impact of inaccurate demand beliefs on dynamics of a Triopoly game is studied. We suppose that all the players make their own estimations on possible demand with errors. A dynamic Triopoly game with such demand belief is set up. Based on this model, existence and local stable region of the equilibriums are investigated by 3D stable regions of Nash equilibrium point. The complex dynamics, such as bifurcation scenarios and route to chaos, are displayed in 2D bifurcation diagrams, in which e1 and α are negatively related to each other. Basins of attraction are investigated and we found that the attraction domain becomes smaller with the increase in price modification speed, which indicates that all the players’ output must be kept within a certain range so as to keep the system stable. Feedback control method is used to keep the system at an equilibrium state.

  14. Construction of the migration flows forecasting into Russian regions

    Directory of Open Access Journals (Sweden)

    Aleksandr Aleksandrovich Tarasyev

    2013-06-01

    Full Text Available This paper presents a dynamic model that can predict the dynamics of migration flows between source countries and host regions, as well as the dynamics of wage levels there. The model is constructed within the framework of neoclassical economics and human capital theory in continuous time. Thanks to liberalization of migration policy in Russia in 2007, the model could be successfully employed to Russian regions and the Commonwealth of Independent States (CIS, which have visa-free entry regulations with the Russian Federation. Employing the model on statistical data, we forecast the number and origin composition of foreign labor force from the CIS into Russian regions for 2010-2016. The purpose of our further research is to classify migrants by skills

  15. Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system

    International Nuclear Information System (INIS)

    Fang, Tingting; Lahdelma, Risto

    2016-01-01

    Highlights: • Social factor is considered for the linear regression models besides weather file. • Simultaneously optimize all the coefficients for linear regression models. • SARIMA combined with linear regression is used to forecast the heat demand. • The accuracy for both linear regression and time series models are evaluated. - Abstract: Forecasting heat demand is necessary for production and operation planning of district heating (DH) systems. In this study we first propose a simple regression model where the hourly outdoor temperature and wind speed forecast the heat demand. Weekly rhythm of heat consumption as a social component is added to the model to significantly improve the accuracy. The other type of model is the seasonal autoregressive integrated moving average (SARIMA) model with exogenous variables as a combination to take weather factors, and the historical heat consumption data as depending variables. One outstanding advantage of the model is that it peruses the high accuracy for both long-term and short-term forecast by considering both exogenous factors and time series. The forecasting performance of both linear regression models and time series model are evaluated based on real-life heat demand data for the city of Espoo in Finland by out-of-sample tests for the last 20 full weeks of the year. The results indicate that the proposed linear regression model (T168h) using 168-h demand pattern with midweek holidays classified as Saturdays or Sundays gives the highest accuracy and strong robustness among all the tested models based on the tested forecasting horizon and corresponding data. Considering the parsimony of the input, the ease of use and the high accuracy, the proposed T168h model is the best in practice. The heat demand forecasting model can also be developed for individual buildings if automated meter reading customer measurements are available. This would allow forecasting the heat demand based on more accurate heat consumption

  16. Asia-Pacific region's oil demand growth strains refining capacity

    International Nuclear Information System (INIS)

    Anon.

    1993-01-01

    The rapidly industrializing Asia-Pacific region has become a stand-alone economic force and the world's growth market for oil. Japan remains the region's premier economic powerhouse but no longer its only one. And Asia-Pacific economic fortunes have decoupled from the more-mature economies of Europe and the U.S. The East-West Center, Honolulu, in January projected Asia-Pacific average oil demand growth of 3.6%/year through 1999, nearly double what most economists expect for oil demand growth worldwide. Demand reached 14.5 million b/d in 1992, up 862,000 b/d from 1991. East-West Center analysts project demand of 16.4 million b/d in 1995 and 19.1 million b/d by 2000. Not all analysts expect such rapid growth for the region. Edward N. Krapels, president of Energy Security Analysis Inc., Washington, D.C., told a seminar in Houston during March that demand in Southeast Asia will grow at an average of 500,000 b/d/year. The paper discusses economic strength; regional demand; the pressure on refining; the shortfall seen; an opposing view from the Energy Security Analysis; and construction activity

  17. Operational flood forecasting system of Umbria Region "Functional Centre

    Science.gov (United States)

    Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

    2009-04-01

    The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according

  18. Satellite provided customer promises services, a forecast of potential domestic demand through the year 2000. Volume 4: Sensitivity analysis

    Science.gov (United States)

    Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

    1984-03-01

    The overall purpose was to forecast the potential United States domestic telecommunications demand for satellite provided customer promises voice, data and video services through the year 2000, so that this information on service demand would be available to aid in NASA program planning. To accomplish this overall purpose the following objectives were achieved: (1) development of a forecast of the total domestic telecommunications demand; (2) identification of that portion of the telecommunications demand suitable for transmission by satellite systems; (3) identification of that portion of the satellite market addressable by consumer promises service (CPS) systems; (4) identification of that portion of the satellite market addressable by Ka-band CPS system; and (5) postulation of a Ka-band CPS network on a nationwide and local level. The approach employed included the use of a variety of forecasting models, a parametric cost model, a market distribution model and a network optimization model. Forecasts were developed for: 1980, 1990, and 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

  19. Using adaptive network based fuzzy inference system to forecast regional electricity loads

    International Nuclear Information System (INIS)

    Ying, L.-C.; Pan, M.-C.

    2008-01-01

    Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads

  20. Using adaptive network based fuzzy inference system to forecast regional electricity loads

    Energy Technology Data Exchange (ETDEWEB)

    Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

    2008-02-15

    Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

  1. The forecast of primary energy demand and electricity demand and the participation of coal in covering this demand; Prognoza zapotrzebowania na energie pierwotna i elektryczna oraz udziat wegla w pokryciu tego zapotrzebowania

    Energy Technology Data Exchange (ETDEWEB)

    Solinski, J.

    2004-07-01

    The paper presents a preliminary forecast of Poland's future coal demand until 2030, particularly the demand for electric power. Two scenarios are examined - one of average GDP growth rate of 3.5% and a second of 4.5%. Implementation of the first scenario would enable Poland to achieve in 2030 today's levels of per capita electricity consumption in main EU countries, with a forecast consumption level of 280 TWh. By 2030, coal's share in electricity production would fall to about 7%, the remainder being from gas, nuclear and renewable sources. 11 refs., 5 tabs.

  2. A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

    Science.gov (United States)

    Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M

    2015-10-01

    To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Forecasting drug utilization and expenditure in a metropolitan health region

    Directory of Open Access Journals (Sweden)

    Korkmaz Seher

    2010-05-01

    Full Text Available Abstract Background New pharmacological therapies are challenging the healthcare systems, and there is an increasing need to assess their therapeutic value in relation to existing alternatives as well as their potential budget impact. Consequently, new models to introduce drugs in healthcare are urgently needed. In the metropolitan health region of Stockholm, Sweden, a model has been developed including early warning (horizon scanning, forecasting of drug utilization and expenditure, critical drug evaluation as well as structured programs for the introduction and follow-up of new drugs. The aim of this paper is to present the forecasting model and the predicted growth in all therapeutic areas in 2010 and 2011. Methods Linear regression analysis was applied to aggregate sales data on hospital sales and dispensed drugs in ambulatory care, including both reimbursed expenditure and patient co-payment. The linear regression was applied on each pharmacological group based on four observations 2006-2009, and the crude predictions estimated for the coming two years 2010-2011. The crude predictions were then adjusted for factors likely to increase or decrease future utilization and expenditure, such as patent expiries, new drugs to be launched or new guidelines from national bodies or the regional Drug and Therapeutics Committee. The assessment included a close collaboration with clinical, clinical pharmacological and pharmaceutical experts from the regional Drug and Therapeutics Committee. Results The annual increase in total expenditure for prescription and hospital drugs was predicted to be 2.0% in 2010 and 4.0% in 2011. Expenditures will increase in most therapeutic areas, but most predominantly for antineoplastic and immune modulating agents as well as drugs for the nervous system, infectious diseases, and blood and blood-forming organs. Conclusions The utilisation and expenditure of drugs is difficult to forecast due to uncertainties about the rate

  4. Forecasting the development of regional economy on the basis of input — output tables

    Directory of Open Access Journals (Sweden)

    Yury Konstantinovich Mashunin

    2014-06-01

    Full Text Available The article presents a practical technology of forecasting the development of the regional economy, including the statement of the problem, the construction of a mathematical model, and its implementation. At the constructing of a model, the standard statistical data for the previous period (2011, built on the basis of the table “input — output” are used. A unit of output of final demand, resulting from investments is added. As a result, a model of the regional economy made in the form of a vector mathematical programming problem that takes into account the investment processes in a region is obtained. Its purpose is to maximize the production of final demand in a region (all industries in a region within the constraints of the input-output balance, investments, resource costs and capacities. For solving linear programming problems of vector, methods, based on the principle of normalization criteria and guaranteed result are used. Vector dynamics problem is solved in a specified number of years. The factors taking into account the rate of growth: gross volumes (resources, final demand, investment in every sector of the region are introduced. Numerical implementation of the prediction is shown in the test case economic modeling of Primorsky Krai, including fifteen branches of a three-year period in accordance with the requirements of the Budget Code. Results of the solution include the major economic indicators for a region: gross, gross regional product (GRP, investments (including broken by industry, as well as payroll taxes and other. All these economic indicators are the basis for the formation of budget revenues in a region.

  5. The Impact of Economic Parameter Uncertainty Growth on Regional Energy Demand Assessment

    Directory of Open Access Journals (Sweden)

    Olga Vasilyevna Mazurova

    2017-06-01

    Full Text Available The article deals with the forecasting studies based on the energy demand and prices in the region in terms of the complex interconnections between economy (and energy and the growth of uncertainty of the future development of the country and territories. The authors propose a methodological approach, which combines the assessment of the price elasticity of energy demand with the optimization of energy and fuel regional supply. In this case, the price elasticity of demand is determined taking into account the comparison of cost-effectiveness of using different types of fuel and energy by different consumers. The originality of the proposed approach consists in simulating the behaviour of suppliers’ (energy companies and large customers’ (power plants, boiler rooms, industry, transport, population depending on energy price changes, the existing and new technologies, energy-saving activities and restrictions on fuel supplies. To take into account the uncertainty of future economic and energy conditions, some parameters such as prospective technical and economic parameters, price, technological parameters are set as the intervals of possible values with different probability levels. This approach allows making multivariate studies with different combinations of the expected conditions and receiving as a result the range of the projected values of studied indicators. The multivariate calculations show that the fuel demand has a nonlinear dependence on the consumer characteristics, pricing, projection horizon, and the nature of the future conditions uncertainty. The authors have shown that this effect can be significant and should be considered in the forecasts of the development of fuel and energy sector. The methodological approach and quantitative evaluation can be used to improve the economic and energy development strategies of the country and regions

  6. Economic growth, regional disparities and energy demand in China

    International Nuclear Information System (INIS)

    Sheng, Yu; Shi, Xunpeng; Zhang, Dandan

    2014-01-01

    Using the panel data of 27 provinces between 1978 and 2008, we employed a instrumental regression technique to examine the relationship between economic growth, energy demand/production and the related policies in China. The empirical results show that forming a cross-province integrated energy market will in general reduce the response of equilibrium user costs of energy products to their local demand and production, through cross-regional energy transfer (including both energy trade and cross-regional reallocation). In particular, reducing transportation costs and improving marketization level are identified as two important policy instruments to enhance the role of energy market integration. The findings support the argument for a more competitive cross-province energy transfer policies and calls for more developed energy connectivity and associate institutional arrangements within China. These policy implications may also be extended to the East Asia Summit region where energy market integration is being actively promoted. - Highlights: • Development driving energy demand has different impacts on energy prices than others. • EMI will reduce the response of equilibrium energy prices to local demand and production. • Reducing transportation costs and improving marketization level enhance the role of EMI. • More market competition and better physical and institutional connectivity are better. • Policy implications to China may be extended to the East Asia Summit region

  7. INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

    Directory of Open Access Journals (Sweden)

    S. Saravanan

    2012-07-01

    Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.

  8. Development of the Manpower Demand Forecast Model of Nuclear Industry Using the System Dynamics Method - Operation Sector

    International Nuclear Information System (INIS)

    Lee, Yong Suk; Ahn, Nam Sung

    2010-01-01

    Recently, the resource management of nuclear engineering manpower has become an important issue in Korean nuclear industry. The government's plan for increasing the number of domestic nuclear power plants and the recent success of nuclear power plant export to UAE (United Arab Emirates) will increase demand for nuclear engineers in Korea. Accordingly, the Korean government decided to supplement 2,246 engineers in the public sector of nuclear industry in the year 2010 to resolve the manpower shortage problem in the short term. However, the experienced engineers which are essentially important in the nuclear industry cannot be supplied in the short term. Therefore, development of the long term manpower demand forecast model of nuclear industry is needed. The system dynamics (SD) is useful method for forecasting nuclear manpower demand. It is because the time-delays which is important in constructing plants and in recruiting and training of engineers, and the feedback effect including the qualitative factor can be effectively considered in the SD method. Especially, the qualitative factor like 'Productivity' is very important concept in Human Resource Management (HRM) but it cannot be easily considered in the other methods. In this paper, the concepts of the nuclear manpower demand forecast model using the SD method are presented and the some simulation results are being discussed especially for the 'Operation Sector'

  9. Development of the Manpower Demand Forecast Model of Nuclear Industry Using the System Dynamics Method - Operation Sector

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Yong Suk [Future and Challenges Inc., Seoul (Korea, Republic of); Ahn, Nam Sung [SolBridge International School of Business, Daejeon (Korea, Republic of)

    2010-10-15

    Recently, the resource management of nuclear engineering manpower has become an important issue in Korean nuclear industry. The government's plan for increasing the number of domestic nuclear power plants and the recent success of nuclear power plant export to UAE (United Arab Emirates) will increase demand for nuclear engineers in Korea. Accordingly, the Korean government decided to supplement 2,246 engineers in the public sector of nuclear industry in the year 2010 to resolve the manpower shortage problem in the short term. However, the experienced engineers which are essentially important in the nuclear industry cannot be supplied in the short term. Therefore, development of the long term manpower demand forecast model of nuclear industry is needed. The system dynamics (SD) is useful method for forecasting nuclear manpower demand. It is because the time-delays which is important in constructing plants and in recruiting and training of engineers, and the feedback effect including the qualitative factor can be effectively considered in the SD method. Especially, the qualitative factor like 'Productivity' is very important concept in Human Resource Management (HRM) but it cannot be easily considered in the other methods. In this paper, the concepts of the nuclear manpower demand forecast model using the SD method are presented and the some simulation results are being discussed especially for the 'Operation Sector'

  10. Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update

    Directory of Open Access Journals (Sweden)

    Chul-Yong Lee

    2017-08-01

    Full Text Available The forecasting demand for new technology for which few historical data observations are available is difficult but essential to sustainable development. The current study suggests an alternative forecasting methodology based on a hazard rate model using stated and revealed preferences of consumers. In estimating the hazard rate, information is initially derived through conjoint analysis based on a consumer survey and then updated using Bayes’ theorem with available market data. To compare the proposed models’ performance with benchmark models, the Bass model, the logistic growth model, and a Bayesian approach based on analogy are adopted. The results show that the proposed model outperforms the benchmark models in terms of pre-launch and post-launch forecasting performances.

  11. Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting

    International Nuclear Information System (INIS)

    Ardakani, F.J.; Ardehali, M.M.

    2014-01-01

    Highlights: • Novel effects of DSM data on electricity consumption forecasting is examined. • Optimal ANN models based on IPSO and SFL algorithms are developed. • Addition of DSM data to socio-economic indicators data reduces MAPE by 36%. - Abstract: Worldwide implementation of demand side management (DSM) programs has had positive impacts on electrical energy consumption (EEC) and the examination of their effects on long-term forecasting is warranted. The objective of this study is to investigate the effects of historical DSM data on accuracy of EEC modeling and long-term forecasting. To achieve the objective, optimal artificial neural network (ANN) models based on improved particle swarm optimization (IPSO) and shuffled frog-leaping (SFL) algorithms are developed for EEC forecasting. For long-term EEC modeling and forecasting for the U.S. for 2010–2030, two historical data types used in conjunction with developed models include (i) EEC and (ii) socio-economic indicators, namely, gross domestic product, energy imports, energy exports, and population for 1967–2009 period. Simulation results from IPSO-ANN and SFL-ANN models show that using socio-economic indicators as input data achieves lower mean absolute percentage error (MAPE) for long-term EEC forecasting, as compared with EEC data. Based on IPSO-ANN, it is found that, for the U.S. EEC long-term forecasting, the addition of DSM data to socio-economic indicators data reduces MAPE by 36% and results in the estimated difference of 3592.8 MBOE (5849.9 TW h) in EEC for 2010–2030

  12. A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information

    OpenAIRE

    Sahu, Sujit K.; Baffour, Bernard; Minty, John; Harper, Paul; Sarran, Christophe

    2013-01-01

    The effect of weather on health has been widely researched, and the ability to forecast meteorological events is able to offer valuable insights into the impact on public health services. In addition, better predictions of hospital demand that are more sensitive to fluctuations in weather can allow hospital administrators to optimise resource allocation and service delivery. Using historical hospital admission data and several seasonal and meteorological variables for a site near the hospital...

  13. Research on strategy and optimization method of PRT empty vehicles resource allocation based on traffic demand forecast

    Science.gov (United States)

    Xiang, Yu; Tao, Cheng

    2018-05-01

    During the operation of the personal rapid transit system(PRT), the empty vehicle resources is distributed unevenly because of different passenger demand. In order to maintain the balance between supply and demand, and to meet the passenger needs of the ride, PRT empty vehicle resource allocation model is constructed based on the future demand forecasted by historical demand in this paper. The improved genetic algorithm is implied in distribution of the empty vehicle which can reduce the customers waiting time and improve the operation efficiency of the PRT system so that all passengers can take the PRT vehicles in the shortest time. The experimental result shows that the improved genetic algorithm can allocate the empty vehicle from the system level optimally, and realize the distribution of the empty vehicle resources reasonably in the system.

  14. Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

    Directory of Open Access Journals (Sweden)

    Yi Liang

    2016-11-01

    Full Text Available The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD with induced ordered weighted harmonic averaging operator (IOWHA to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM forecasting model and multiple regression (MR model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.

  15. The Challenge of Forecasting Metropolitan Growth: Urban Characteristics Based Models versus Regional Dummy Based Models

    OpenAIRE

    NA

    2005-01-01

    This paper presents a study of errors in forecasting the population of Metropolitan Statistical Areas and the Primary MSAs of Consolidated Metropolitan Statistical Areas and New England MAs. The forecasts are for the year 2000 and are based on a semi-structural model estimated by Mills and Lubelle using 1970 to 1990 census data on population, employment and relative real wages. This model allows the testing of regional effects on population and employment growth. The year 2000 forecasts are f...

  16. Short term forecasting of petroleum product demand in France; Modelisation a court terme des consommations de produits petroliers en France

    Energy Technology Data Exchange (ETDEWEB)

    Cadren, M

    1998-06-23

    The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It`s filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter`s. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach`s and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author) 153 refs.

  17. Peculiarities in the demand forecast for an HSRL connecting two countries. Case of Kuala Lumpur Singapore HSRL

    Energy Technology Data Exchange (ETDEWEB)

    Tadeo Martin, A.; Rey Romero, P.

    2016-07-01

    The High Speed Rail Line (HSRL) between Kuala Lumpur (KL) and Singapore (SP) is aimed at reducing to 90 minutes the travel time between two of Southeast Asia’s most vibrant and fast-growing economic engines. Ineco was hired by the Government of Malaysia to forecast the demand for the future HSRL. The Government of Malaysia played a key role when firstly defining the current situation on the corridor and the features of the HSRL, and then providing Ineco with the studies previously developed by them. Besides the obvious interest in connecting both capital cities, Malaysia wishes to promote the economic development of intermediate cities, whereas Singapore, a 718 square-kilometer city-state and island, is interested in improving its connection with Nusajaya - a Malaysian city that is being developed just across the border and regarded as land reserves. Two kinds of services will be provided on the new rail infrastructure: non-stop services between KL and SP, and services with 6 intermediate stops on the Malaysian side. The aim of this paper is to describe the process to forecast the demand for the HSRL Kuala Lumpur – Singapore by focusing on the peculiarities of an international HSRL. Identifying these aspects at an early stage is vital to get a better demand estimate and to reconsider the HSRL characteristics if necessary. The demand for the new line was calculated by applying a three-step model: generation model, distribution model and modal split model. In 2030, 10 years after the opening year, the HSRL is expected to move 23 - 26 million passengers – baseline and optimistic scenarios, respectively -, which represents an 18.5% share of the total demand on the corridor. The demand for the KL-SP pair will account for 30% the demand for the future HSRL. (Author)

  18. Australia's long-term electricity demand forecasting using deep neural networks

    OpenAIRE

    Hamedmoghadam, Homayoun; Joorabloo, Nima; Jalili, Mahdi

    2018-01-01

    Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand under-estimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep neural networks to predict Australia's long-term electricity demand. A stacked autoencoder is used in...

  19. Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

    Science.gov (United States)

    Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

    2017-12-01

    Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the Mizu

  20. Water demand studies. [central and southern California regions

    Science.gov (United States)

    Bowden, L. W.; Estes, J. E.; Churchman, C. W.; Johnson, C. W.; Huning, J. R.; Rozelle, K.; Hamilton, J.; Washburn, G.; Tinney, L. R.; Thaman, R. R.

    1973-01-01

    The areas of focus of the Santa Barbara and Riverside groups in conducting water demand studies are the central and southern California regional test sites, respectively. Within each test site, sub-areas have been selected for use in the making of detailed investigations. Within each of these sub-areas an in-depth evaluation is being made as to the capability of remote sensing systems to provide pertinent data relative to water demand phenomena. These more limited sub-areas are: (1) Kern County and the San Joaquin Basin; (2) Chino-Riverside Basin; and (3) the Imperial Valley. Rational for the selection of these subareas included the following: Much of the previous remote sensing research had been conducted in these areas and therefore a great deal of remote sensing imagery and pertinent ground truth for the areas was already available.

  1. Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms

    International Nuclear Information System (INIS)

    Piltan, Mehdi; Shiri, Hiva; Ghaderi, S.F.

    2012-01-01

    Highlights: ► Investigating different fitness functions for evolutionary algorithms in energy forecasting. ► Energy forecasting of Iranian metal industry by value added, energy prices, investment and employees. ► Using real-coded instead of binary-coded genetic algorithm decreases energy forecasting error. - Abstract: Developing energy-forecasting models is known as one of the most important steps in long-term planning. In order to achieve sustainable energy supply toward economic development and social welfare, it is required to apply precise forecasting model. Applying artificial intelligent models for estimation complex economic and social functions is growing up considerably in many researches recently. In this paper, energy consumption in industrial sector as one of the critical sectors in the consumption of energy has been investigated. Two linear and three nonlinear functions have been used in order to forecast and analyze energy in the Iranian metal industry, Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) are applied to attain parameters of the models. The Real-Coded Genetic Algorithm (RCGA) has been developed based on real numbers, which is introduced as a new approach in the field of energy forecasting. In the proposed model, electricity consumption has been considered as a function of different variables such as electricity tariff, manufacturing value added, prevailing fuel prices, the number of employees, the investment in equipment and consumption in the previous years. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE) are the four functions which have been used as the fitness function in the evolutionary algorithms. The results show that the logarithmic nonlinear model using PSO algorithm with 1.91 error percentage has the best answer. Furthermore, the prediction of electricity consumption in industrial sector of Turkey and also Turkish industrial sector

  2. Do regional weather models contribute to better wind power forecasts? A few Norwegian case studies

    DEFF Research Database (Denmark)

    Bremnes, John Bjørnar; Giebel, Gregor

    2017-01-01

    resolution of this grid determines how accurate meteorological processes can be modeled and thereby also limits forecast quality. In this study, two global and four regional operational NWP models with spatial horizontal resolutions ranging from 1 to 32 km were applied to make wind power forecasts up to 66...

  3. The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

    Science.gov (United States)

    Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

    2014-01-01

    The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the

  4. Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

    Energy Technology Data Exchange (ETDEWEB)

    Yousefi, M.; Omid, M.; Rafiee, Sh. [Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of); Ghaderi, S. F. [Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2013-07-01

    Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.

  5. Optimal Ordering Policy and Coordination Mechanism of a Supply Chain with Controllable Lead-Time-Dependent Demand Forecast

    Directory of Open Access Journals (Sweden)

    Hua-Ming Song

    2011-01-01

    Full Text Available This paper investigates the ordering decisions and coordination mechanism for a distributed short-life-cycle supply chain. The objective is to maximize the whole supply chain's expected profit and meanwhile make the supply chain participants achieve a Pareto improvement. We treat lead time as a controllable variable, thus the demand forecast is dependent on lead time: the shorter lead time, the better forecast. Moreover, optimal decision-making models for lead time and order quantity are formulated and compared in the decentralized and centralized cases. Besides, a three-parameter contract is proposed to coordinate the supply chain and alleviate the double margin in the decentralized scenario. In addition, based on the analysis of the models, we develop an algorithmic procedure to find the optimal ordering decisions. Finally, a numerical example is also presented to illustrate the results.

  6. Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

    Energy Technology Data Exchange (ETDEWEB)

    Yousefi, M.; Omid, M.; Rafiee, Sh. [Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of); Ghaderi, S.F. [Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2013-07-01

    Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.

  7. Commercial demand for energy: a disaggregated approach. [Model validation for 1970-1975; forecasting to 2000

    Energy Technology Data Exchange (ETDEWEB)

    Jackson, J.R.; Cohn, S.; Cope, J.; Johnson, W.S.

    1978-04-01

    This report describes the structure and forecasting accuracy of a disaggregated model of commercial energy use recently developed at Oak Ridge National Laboratory. The model forecasts annual commercial energy use by ten building types, five end uses, and four fuel types. Both economic (utilization rate, fuel choice, capital-energy substitution) and technological factors (equipment efficiency, thermal characteristics of buildings) are explicitly represented in the model. Model parameters are derived from engineering and econometric analysis. The model is then validated by simulating commercial energy use over the 1970--1975 time period. The model performs well both with respect to size of forecast error and ability to predict turning points. The model is then used to evaluate the energy-use implications of national commercial buildings standards based on the ASHRAE 90-75 recommendations. 10 figs., 12 tables, 14 refs.

  8. Supply shortage forecast in Ontario: The significance of demand-side management (DSM); its tools and techniques

    International Nuclear Information System (INIS)

    Saini, S.

    2004-01-01

    Aspects of the recent report by the Ontario Electricity Conservation and Supply Task Force and Independent Market Operator which forecasts acute power supply shortages in Ontario, are discussed. Immediate action is recommended to avert the problem. The principal recommendation concerns the adoption of Demand Side Management as a tool to reduce the widening gap between supply and demand, citing supply shortage, imports, high prices, deregulated market and environmental concerns as the driving forces which push for the adoption of DSM. It is claimed that DSM, through its tools such as Demand/Load Response Programs and Time-of-Use rates has the capacity to create the necessary balance between supply and demand more efficiently, and in a more timely fashion than supply side management. The demand for adoption of DSM is justified on the basis of a careful examination of the magnitude and significance of each of the driving forces affecting the electricity supply in Ontario, as well as the benefits and techniques of DSM designed to manage power shortages. Energy Conservation and Efficiency and Demand/Load Response Programs are discussed as the principal DSM techniques, while Dynamic/Real Time Pricing, Time-of-Use Rates, Automated /Smart Metering, Web-based/Communication Systems, Reliability-based Programs, Market/Price-based programs, and Types of Load Control are described as the principal tools used by DSM. DSM program approaches and strategies are also reviewed, along with a brief list of successful examples of DSM applications. 3 figs

  9. Supply shortage forecast in Ontario: The significance of demand-side management (DSM); its tools and techniques

    Energy Technology Data Exchange (ETDEWEB)

    Saini, S.

    2004-06-01

    Aspects of the recent report by the Ontario Electricity Conservation and Supply Task Force and Independent Market Operator which forecasts acute power supply shortages in Ontario, are discussed. Immediate action is recommended to avert the problem. The principal recommendation concerns the adoption of Demand Side Management as a tool to reduce the widening gap between supply and demand, citing supply shortage, imports, high prices, deregulated market and environmental concerns as the driving forces which push for the adoption of DSM. It is claimed that DSM, through its tools such as Demand/Load Response Programs and Time-of-Use rates has the capacity to create the necessary balance between supply and demand more efficiently, and in a more timely fashion than supply side management. The demand for adoption of DSM is justified on the basis of a careful examination of the magnitude and significance of each of the driving forces affecting the electricity supply in Ontario, as well as the benefits and techniques of DSM designed to manage power shortages. Energy Conservation and Efficiency and Demand/Load Response Programs are discussed as the principal DSM techniques, while Dynamic/Real Time Pricing, Time-of-Use Rates, Automated /Smart Metering, Web-based/Communication Systems, Reliability-based Programs, Market/Price-based programs, and Types of Load Control are described as the principal tools used by DSM. DSM program approaches and strategies are also reviewed, along with a brief list of successful examples of DSM applications. 3 figs.

  10. A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

    International Nuclear Information System (INIS)

    Kıran, Mustafa Servet; Özceylan, Eren; Gündüz, Mesut; Paksoy, Turan

    2012-01-01

    Highlights: ► PSO and ACO algorithms are hybridized for forecasting energy demands of Turkey. ► Linear and quadratic forms are developed to meet the fluctuations of indicators. ► GDP, population, export and import have significant impacts on energy demand. ► Quadratic form provides better fit solution than linear form. ► Proposed approach gives lower estimation error than ACO and PSO, separately. - Abstract: This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.

  11. Enhanced regional forecasting considering single wind farm distribution for upscaling

    International Nuclear Information System (INIS)

    Bremen, Lueder von; Saleck, Nadja; Heinemann, Detlev

    2007-01-01

    With increasing wind power penetration the need for more accurate wind power forecasts increases to raise the market value of wind power. State-of-the-art wind power forecasting tools are considered either statistical or physical. Fundamentally new techniques are rare, thus it is tried to establish a new approach. The spatial decomposition of wind power generation in Germany can be done with principle component analysis to extract the main pattern of variability. They have a physical meaning when linked with typical weather situation. The first four eigenvectors explain about 94 % of the observed variance. The time-evolving principle components are linked with the total wind power feed-in in Germany and are used for its estimation. A new wind power forecasting model has been implemented with this approach and shows very good results that are comparable with state-of-the-art commercial wind power forecast models. The day-ahead forecast error for a common intercomparison period Jan-Jul 2006 is 4.4 %. The suggested approach offers wide ranges for future developments (e.g. several NWP models), because it is computationally very cheap to run

  12. Forecasting inter-urban transport demand for a logistics company: A combined grey–periodic extension model with remnant correction

    Directory of Open Access Journals (Sweden)

    Donghui Wang

    2015-12-01

    Full Text Available Accurately predicting short-term transport demand for an individual logistics company involved in a competitive market is critical to make short-term operation decisions. This article proposes a combined grey–periodic extension model with remnant correction to forecast the short-term inter-urban transport demand of a logistics company involved in a nationwide competitive market, showing changes in trend and seasonal fluctuations with irregular periods different to the macroeconomic cycle. A basic grey–periodic extension model of an additive pattern, namely, the main combination model, is first constructed to fit the changing trends and the featured seasonal fluctuation periods. In order to improve prediction accuracy and model adaptability, the grey model is repeatedly modelled to fit the remnant tail time series of the main combination model until prediction accuracy is satisfied. The modelling approach is applied to a logistics company engaged in a nationwide less-than-truckload road transportation business in China. The results demonstrate that the proposed modelling approach produces good forecasting results and goodness of fit, also showing good model adaptability to the analysed object in a changing macro environment. This fact makes this modelling approach an option to analyse the short-term transportation demand of an individual logistics company.

  13. Micro-generation dispatch in a smart residential multi-carrier energy system considering demand forecast error

    International Nuclear Information System (INIS)

    Sanjari, M.J.; Karami, H.; Gooi, H.B.

    2016-01-01

    Highlights: • Combination of day-ahead and hour-ahead optimizations to design online controller. • Investigating the effect of load forecast error on the system operating cost. • Proposing effective method for hour-ahead resource re-dispatch. • Using the HSS algorithm as a powerful and effective optimization method. • Combining long-term and short-term strategies for optimal dispatch of resources. - Abstract: This paper deals with a residential hybrid thermal/electrical grid-connected home energy system incorporating real data for the load demand. A day-ahead scheduling (DAS) algorithm for dispatching different resources has been developed in previous studies to determine the optimal operation scheduling for the distributed energy resources at each time interval so that the operational cost of a smart house is minimized. However, demand of houses may be changed in each hour and cannot be exactly predicted one day ahead. System complexity caused by nonlinear dynamics of the fuel cell, as a combined heat and power device, and battery charging and discharging time make it difficult to find the optimal operating point of the system by using the optimization algorithms quickly in online applications. In this paper, the demand forecast error is studied and a near-optimal dispatch strategy by using artificial neural network (ANN) is proposed for the residential energy system when the demand changes are known one hour ahead with respect to the predicted day-ahead values. The day-ahead and hour-ahead optimizations are combined and ANN training inputs are adjusted according to the problem such that the economic dispatch of different energy resources can be achieved by the proposed method compared with previous studies. Using the model of the fuel cell extracted from experimental measurement and real data for the load demand makes the results more applicable in real residential energy systems.

  14. Forecasting water demand using back propagation networks in the operation of reservoirs in the citarum cascade, west java, indonesia

    Directory of Open Access Journals (Sweden)

    Mulya R. Mashudi

    2017-11-01

    Full Text Available This study investigates the use of Neural Networks (NN as a potential means of more accurately forecasting water demand in the Citarum River basin cascade. Neural Networks have the ability to recognise nonlinear patterns when sufficiently trained with historical data. The study constructs a NN model of the cascade, based on Back Propagation Networks (BPN. Data representing physical characteristics and meteorological conditions in the Citarum River basin from 1989 through 1995 were used to train the BPN. Nonlinear activation functions (sigmoid, tangent, and gaussian functions and hidden layers in the BPN were chosen for the study.

  15. Handbook for Forecasters in the Mediterranean. Part 2. Regional Forecasting Aids for the Mediterranean Basin.

    Science.gov (United States)

    1980-12-01

    important role in modifying the poniente. Strong northwesterly flow crossing the Sierra Nevada (see Figure 1-1) causes a lee trough to develop along...the coast of southern France. *Comprises British forecast sea areas Lions, Unicorn , Bougie; see Figure lb in the Introduction. II-I 0) (1) C- C.0 CDC L...Alps are most important features insofar as they play a significant weather role in terms of planetary waves, cyclogenesis and fronts. 1 .2 SEASONAL

  16. Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

    Energy Technology Data Exchange (ETDEWEB)

    Garikapati, Venu M. [Systems Analysis and Integration Section, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401; You, Daehyun [Maricopa Association of Governments, 302 North First Avenue, Suite 300, Phoenix, AZ 85003; Zhang, Wenwen [School of City and Regional Planning, Center for Geographic Information Systems, Georgia Institute of Technology, 760 Spring Street, Suite 230, Atlanta, GA 30308; Pendyala, Ram M. [School of Sustainable Engineering and the Built Environment, Arizona State University, 660 South College Avenue, Tempe, AZ 85281; Guhathakurta, Subhrajit [School of City and Regional Planning, Center for Geographic Information Systems, Georgia Institute of Technology, 760 Spring Street, Suite 230, Atlanta, GA 30308; Brown, Marilyn A. [School of Public Policy, 685 Cherry Street, Georgia Institute of Technology, Atlanta, GA 30332; Dilkina, Bistra [School of Computational Science and Engineering, 266 Ferst Drive, Georgia Institute of Technology, Atlanta, GA 30332

    2017-01-01

    This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

  17. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

    Science.gov (United States)

    Khan, Muhammad Yousaf; Mittnik, Stefan

    2018-01-01

    In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

  18. Forecasted electric power demands for the Baltimore Gas and Electric Company. Volume 1 and Volume 2. Documentation manual

    International Nuclear Information System (INIS)

    Estomin, S.L.; Beach, J.E.; Goldsmith, J.V.

    1991-05-01

    The two-volume report presents the results of an econometric forecast of peak load and electric power demand for the Baltimore Gas and Electric Company (BG ampersand E) through the year 2009. Separate energy sales models were estimated for residential sales in Baltimore City, residential sales in the BG ampersand E service area excluding Baltimore City, commercial sales, industrial sales, streetlighting sales, and Company use plus losses. Econometric equations were also estimated for electric space heating and air conditioning saturation in Baltimore City and in the remainder of the BG ampersand E service territory. In addition to the energy sales models and the electric space conditioning saturation models, econometric models of summer and winter peak demand on the BG ampersand E system were estimated

  19. Forecasted electric power demands for the Delmarva Power and Light Company. Volume 1 and Volume 2. Documentation manual

    International Nuclear Information System (INIS)

    Estomin, S.L.; Beach, J.E.

    1990-10-01

    The two-volume report presents the results of an econometric forecast of peak load and electric power demands for the Delmarva Power and Light Company (DP ampersand L) through the year 2008. Separate sets of models were estimated for the three jurisdictions served by DP ampersand L: Delaware, Maryland and Virginia. For both Delaware and Maryland, econometric equations were estimated for residential, commercial, industrial, and streetlighting sales. For Virginia, equations were estimated for residential, commercial plus industrial, and streetlighting sales; separate industrial and commercial equations were not estimated for Virginia due to the relatively small size of DP ampersand L's Virginia Industrial load. Wholesale sales were econometrically estimated for the DP ampersand L system as a whole. In addition to the energy sales models, an econometric model of annual (summer) peak demand was estimated for the Company

  20. Forecasting the regional distribution and sufficiency of physicians in Japan with a coupled system dynamics-geographic information system model.

    Science.gov (United States)

    Ishikawa, Tomoki; Fujiwara, Kensuke; Ohba, Hisateru; Suzuki, Teppei; Ogasawara, Katsuhiko

    2017-09-12

    In Japan, the shortage of physicians has been recognized as a major medical issue. In our previous study, we reported that the absolute shortage will be resolved in the long term, but maldistribution among specialties will persist. To address regional shortage, several Japanese medical schools increased existing quota and established "regional quotas." This study aims to assist policy makers in designing effective policies; we built a model for forecasting physician numbers by region to evaluate future physician supply-demand balances. For our case study, we selected Hokkaido Prefecture in Japan, a region displaying disparities in healthcare services availability between urban and rural areas. We combined a system dynamics (SD) model with geographic information system (GIS) technology to analyze the dynamic change in spatial distribution of indicators. For Hokkaido overall and for each secondary medical service area (SMSA) within the prefecture, we analyzed the total number of practicing physicians. For evaluating absolute shortage and maldistribution, we calculated sufficiency levels and Gini coefficient. Our study covered the period 2010-2030 in 5-year increments. According to our forecast, physician shortage in Hokkaido Prefecture will largely be resolved by 2020. Based on current policies, we forecast that four SMSAs in Hokkaido will continue to experience physician shortages past that date, but only one SMSA would still be understaffed in 2030. The results show the possibility that diminishing imbalances between SMSAs would not necessarily mean that regional maldistribution would be eliminated, as seen from the sufficiency levels of the various SMSAs. Urgent steps should be taken to place doctors in areas where our forecasting model predicts that physician shortages could occur in the future.

  1. An improved method for forecasting spare parts demand using extreme value theory

    NARCIS (Netherlands)

    Zhu, S.; Dekker, R.; van Jaarsveld, W.; Renjie, R.W.; Koning, A.J.

    2017-01-01

    Inventory control for spare parts is essential for many organizations due to the trade-off between preventing high holding cost and stockouts. The lead time demand distribution plays a central role in inventory control. The estimation of this distribution is problematic as the spare part demand is

  2. Workforce Projections 2010-2020: Annual Supply and Demand Forecasting Models for Physical Therapists Across the United States.

    Science.gov (United States)

    Landry, Michel D; Hack, Laurita M; Coulson, Elizabeth; Freburger, Janet; Johnson, Michael P; Katz, Richard; Kerwin, Joanne; Smith, Megan H; Wessman, Henry C Bud; Venskus, Diana G; Sinnott, Patricia L; Goldstein, Marc

    2016-01-01

    Health human resources continue to emerge as a critical health policy issue across the United States. The purpose of this study was to develop a strategy for modeling future workforce projections to serve as a basis for analyzing annual supply of and demand for physical therapists across the United States into 2020. A traditional stock-and-flow methodology or model was developed and populated with publicly available data to produce estimates of supply and demand for physical therapists by 2020. Supply was determined by adding the estimated number of physical therapists and the approximation of new graduates to the number of physical therapists who immigrated, minus US graduates who never passed the licensure examination, and an estimated attrition rate in any given year. Demand was determined by using projected US population with health care insurance multiplied by a demand ratio in any given year. The difference between projected supply and demand represented a shortage or surplus of physical therapists. Three separate projection models were developed based on best available data in the years 2011, 2012, and 2013, respectively. Based on these projections, demand for physical therapists in the United States outstrips supply under most assumptions. Workforce projection methodology research is based on assumptions using imperfect data; therefore, the results must be interpreted in terms of overall trends rather than as precise actuarial data-generated absolute numbers from specified forecasting. Outcomes of this projection study provide a foundation for discussion and debate regarding the most effective and efficient ways to influence supply-side variables so as to position physical therapists to meet current and future population demand. Attrition rates or permanent exits out of the profession can have important supply-side effects and appear to have an effect on predicting future shortage or surplus of physical therapists. © 2016 American Physical Therapy

  3. Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator

    Science.gov (United States)

    Fernández-Vázquez, Esteban; Moreno, Blanca

    2017-10-01

    Forecast combination has been studied in econometrics for a long time, and the literature has shown the superior performance of forecast combination over individual predictions. However, there is still controversy on which is the best procedure to specify the forecast weights. This paper explores the possibility of using a procedure based on Entropy Econometrics, which allows setting the weights for the individual forecasts as a mixture of different alternatives. In particular, we examine the ability of the Data-Weighted Prior Estimator proposed by Golan (J Econom 101(1):165-193, 2001) to combine forecasting models in a context of small sample sizes, a relative common scenario when dealing with time series for regional economies. We test the validity of the proposed approach using a simulation exercise and a real-world example that aims at predicting gross regional product growth rates for a regional economy. The forecasting performance of the Data-Weighted Prior Estimator proposed is compared with other combining methods. The simulation results indicate that in scenarios of heavily ill-conditioned datasets the approach suggested dominates other forecast combination strategies. The empirical results are consistent with the conclusions found in the numerical experiment.

  4. Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California.

    Science.gov (United States)

    Lee, Ya-Ting; Turcotte, Donald L; Holliday, James R; Sachs, Michael K; Rundle, John B; Chen, Chien-Chih; Tiampo, Kristy F

    2011-10-04

    The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M ≥ 4.95 earthquakes in 0.1° × 0.1° cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M ≥ 4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor-Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most "successful" in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts.

  5. A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand

    International Nuclear Information System (INIS)

    Wang Jianzhou; Zhu Wenjin; Zhang Wenyu; Sun Donghuai

    2009-01-01

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined ε-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the ε-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.

  6. A trend fixed on firstly and seasonal adjustment model combined with the epsilon-SVR for short-term forecasting of electricity demand

    Energy Technology Data Exchange (ETDEWEB)

    Wang Jianzhou [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhu Wenjin, E-mail: crying.1@hotmail.co [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

    2009-11-15

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined epsilon-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the epsilon-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.

  7. A trend fixed on firstly and seasonal adjustment model combined with the {epsilon}-SVR for short-term forecasting of electricity demand

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jianzhou; Zhu, Wenjin [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang, Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun, Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

    2009-11-15

    Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined {epsilon}-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the {epsilon}-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved. (author)

  8. Time series regression and ARIMAX for forecasting currency flow at Bank Indonesia in Sulawesi region

    Science.gov (United States)

    Suharsono, Agus; Suhartono, Masyitha, Aulia; Anuravega, Arum

    2015-12-01

    The purpose of the study is to forecast the outflow and inflow of currency at Indonesian Central Bank or Bank Indonesia (BI) in Sulawesi Region. The currency outflow and inflow data tend to have a trend pattern which is influenced by calendar variation effects. Therefore, this research focuses to apply some forecasting methods that could handle calendar variation effects, i.e. Time Series Regression (TSR) and ARIMAX models, and compare the forecast accuracy with ARIMA model. The best model is selected based on the lowest of Root Mean Squares Errors (RMSE) at out-sample dataset. The results show that ARIMA is the best model for forecasting the currency outflow and inflow at South Sulawesi. Whereas, the best model for forecasting the currency outflow at Central Sulawesi and Southeast Sulawesi, and for forecasting the currency inflow at South Sulawesi and North Sulawesi is TSR. Additionally, ARIMAX is the best model for forecasting the currency outflow at North Sulawesi. Hence, the results show that more complex models do not neccessary yield more accurate forecast than the simpler one.

  9. Development of demand functions and their inclusion in linear programming forecasting models

    International Nuclear Information System (INIS)

    Chamberlin, J.H.

    1976-05-01

    The purpose of the paper is to present a method for including demand directly within a linear programming model, and to use this method to analyze the effect of the Liquid Metal Fast Breeder Reactor upon the nuclear energy system

  10. DESIGNING A SUPPLY CHAIN MODEL WITH CONSIDERATION DEMAND FORECASTING AND INFORMATION SHARING

    OpenAIRE

    S.M.T. Fatemi Ghomi; N. Azad

    2012-01-01

    ENGLISH ABSTRACT: In traditional supply chain inventory management, orders are the only information firms exchange, but information technology now allows firms to share demand and inventory data quickly and inexpensively. To have an integrated plan, a manufacturer not only needs to know demand information from its customers but also supply information from its suppliers. In this paper, information flow is incorporated in a three-echelon supply chain model. Also to decrease the risk o...

  11. Wave ensemble forecast system for tropical cyclones in the Australian region

    Science.gov (United States)

    Zieger, Stefan; Greenslade, Diana; Kepert, Jeffrey D.

    2018-05-01

    Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4∘) and regional (resolution 1/10∘) domains with forecast ranges of + 7 and + 3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing + 10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.

  12. Regional PV power estimation and forecast to mitigate the impact of high photovoltaic penetration on electric grid.

    Science.gov (United States)

    Pierro, Marco; De Felice, Matteo; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina

    2017-04-01

    The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid and increase the amount of energy reserve and the energy imbalance cost. On regional scale, solar power estimation and forecast is becoming essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators of generation. Indeed the estimation of regional PV power can be used for PV power supervision and real time control of residual load. Mid-term PV power forecast can be employed for transmission scheduling to reduce energy imbalance and related cost of penalties, residual load tracking, trading optimization, secondary energy reserve assessment. In this context, a new upscaling method was developed and used for estimation and mid-term forecast of the photovoltaic distributed generation in a small area in the north of Italy under the control of a local DSO. The method was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. It requires a low computational effort and very few input information should be provided by users. The power estimation model achieved a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% while the one and two days forecast achieve to a RMSE of 7% and 7.5%. A model to estimate the forecast error and the prediction intervals was also developed. The photovoltaic production in the considered region provided the 6.9% of the electric consumption in 2015. Since the PV penetration is very similar to the one observed at national level (7.9%), this is a good case study to analyse the impact of PV generation on the electric grid and the effects of PV power forecast on transmission scheduling and on secondary reserve estimation. It appears that, already with 7% of PV

  13. Electric power supply and demand 1979 to 1988 for the contiguous United States as projected by the Regional Electric Reliability Councils in their April 1, 1979 long-range coordinated planning reports to the Department of Energy

    Energy Technology Data Exchange (ETDEWEB)

    Savage, N.; Graban, W.

    1979-12-01

    Information concerning bulk electric power supply and demand is summarized and reviewed. Electric-utility power-supply systems are composed of power sources, transmission and distribution facilities, and users of electricity. In the United States there are three such systems of large geographic extent that together cover the entire country. Subjects covered are: energy forecasts, peak demand forecasts, generating-capacity forecasts, purchases and sales of capacity, and transmission. Extensive data are compiled in 17 tables. Information in two appendices includes a general description of the Regional Electric Reliability Councils and US generating capacity as of June 30, 1979. 3 figures, 17 tables.

  14. The Supply of Medical Radioisotopes. Medical Isotope Supply in the Future: Production Capacity and Demand. Forecast for the 99Mo/99mTc Market, 2015-2020

    International Nuclear Information System (INIS)

    Peykov, Pavel; Cameron, Ron

    2014-04-01

    This document presents a forecast of 99 Mo/ 99m Tc production capacity and demand in 2015-2020, when two major irradiators - the OSIRIS and NRU reactors in France and Canada - are expected to exit the global supply chain and new alternative technology projects may be commissioned. The forecast does not attempt to predict shortages, but identify periods when there is an increased risk of disrupted supply, to inform policy makers and other stakeholders. (authors)

  15. The long-term forecast of Pakistan's electricity supply and demand: An application of long range energy alternatives planning

    International Nuclear Information System (INIS)

    Perwez, Usama; Sohail, Ahmed; Hassan, Syed Fahad; Zia, Usman

    2015-01-01

    The long-term forecasting of electricity demand and supply has assumed significant importance in fundamental research to provide sustainable solutions to the electricity issues. In this article, we provide an overview of structure of electric power sector of Pakistan and a summary of historical electricity demand & supply data, current status of divergent set of energy policies as a framework for development and application of a LEAP (Long-range Energy Alternate Planning) model of Pakistan's electric power sector. Pakistan's LEAP model is used to analyze the supply policy selections and demand assumptions for future power generation system on the basis of economics, technicality and implicit environmental implications. Three scenarios are enacted over the study period (2011–2030) which include BAU (Business-As-Usual), NC (New Coal) & GF (Green Future). The results of these scenarios are compared in terms of projected electricity demand & supply, net present cost analysis (discount rate at 4%, 7% and 10%) and GHG (greenhouse gas) emission reductions, along with sensitivity analysis to study the effect of varying parameters on total cost. A concluding section illustrates the policy implications of model for futuristic power generation and environmental policies in Pakistan. - Highlights: • Pakistan-specific electricity demand model is presented. • None of the scenarios exceeded the price of 12 US Cents/kWh. • By 2030, fuel cost is the most dominant factor to influence electricity per unit cost. • By 2030, CO_2 emissions per unit electricity will increase significantly in coal scenario relative to others. • By 2030, the penetration of renewable energy and conservation policies can save 70.6 tWh electricity.

  16. The long-term forecast of Taiwan's energy supply and demand: LEAP model application

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Yophy, E-mail: yohuanghaka@gmail.com [Deptartment of Public Finance and Tax Administration, National Taipei College of Business, Taipei Taiwan, 10051 (China); Bor, Yunchang Jeffrey [Deptartment of Economics, Chinese Culture University, Yang-Ming-Shan, Taipei, 11114, Taiwan (China); Peng, Chieh-Yu [Statistics Department, Taoyuan District Court, No. 1 Fazhi Road, Taoyuan City 33053, Taiwan (China)

    2011-11-15

    The long-term forecasting of energy supply and demand is an extremely important topic of fundamental research in Taiwan due to Taiwan's lack of natural resources, dependence on energy imports, and the nation's pursuit of sustainable development. In this article, we provide an overview of energy supply and demand in Taiwan, and a summary of the historical evolution and current status of its energy policies, as background to a description of the preparation and application of a Long-range Energy Alternatives Planning System (LEAP) model of Taiwan's energy sector. The Taiwan LEAP model is used to compare future energy demand and supply patterns, as well as greenhouse gas emissions, for several alternative scenarios of energy policy and energy sector evolution. Results of scenarios featuring 'business-as-usual' policies, aggressive energy-efficiency improvement policies, and on-schedule retirement of Taiwan's three existing nuclear plants are provided and compared, along with sensitivity cases exploring the impacts of lower economic growth assumptions. A concluding section provides an interpretation of the implications of model results for future energy and climate policies in Taiwan. - Research Highlights: > The LEAP model is useful for international energy policy comparison. > Nuclear power plants have significant, positive impacts on CO{sub 2} emission. > The most effective energy policy is to adopt demand-side management. > Reasonable energy pricing provides incentives for energy efficiency and conservation. > Financial crisis has less impact on energy demand than aggressive energy policy.

  17. Analysis of GRI North American Regional Gas Supply-Demand Model

    International Nuclear Information System (INIS)

    Nesbitt, D.M.; Singh, J.; Pine, G.D.; Kline, D.; Barron, M.; Cheung, P.D.

    1989-01-01

    This paper summarizes the results from the GRI North American Regional Gas Supply-Demand Model using the four scenarios defined for the Energy Modeling Forum Number 9 (EMF-9) described in EMF-9 Working Paper 9.4 (1987). The analysis is designed both to showcase the GRI North American Regional model as well as to infer meaningful results about the North American natural gas system. The focus of the analysis is not R ampersand D per se; R ampersand D analysis using the model is conducted regularly by GRI and described elsewhere. Rather, the objective is to analyze some of the major uncertainties in the North American gas market, uncertainties that potentially affect all players including GRI. In particular, the authors seek to quantify the overall economic environment in which production, transmission, distribution, consumption, and R ampersand D decisions will be made and how different that overall environment might be under alternative assumptions. An attendant objective of this analysis has been to enlist economists from a range of organizations (producers, regulators, GRI, and consultants) to carefully scrutinize the GRI North American Regional model and results. In particular, the coauthors were assembled from diverse organizations to review and evaluate model outputs, applying their particular experience and perspective. The four EMF-9 scenarios upon which this paper is based are described in detail later in this document. Briefly, scenario one represents a world with a surfeit of gas and a relatively high oil price projection; scenario two considers a lower oil price forecast; scenario three assumes a pessimistic outlook for the gas resource base with the same oil prices as scenario one; and scenario four examines a higher level of demand for gas in the North American gas market. An important objective of this analysis is to illustrate the predictive power of multi-scenario comparisons (as contrasted with detailed analysis of any individual scenario)

  18. Modeling and forecasting energy consumption in China: Implications for Chinese energy demand and imports in 2020

    International Nuclear Information System (INIS)

    Adams, F. Gerard; Shachmurove, Yochanan

    2008-01-01

    The Chinese economy is in a stage of energy transition: from low efficiency solid fuels to oil, gas, and electric power, from agriculture to urbanization and industrialization, from heavy industry to lighter and high tech industry, from low motorization to rapid growth of the motor vehicle population. Experts fear that continued rapid economic growth in China will translate into a massive need to expand imports of oil, coal, and gas. We build an econometric model of the Chinese energy economy based on the energy balance. We use that model to forecast Chinese energy consumption and imports to 2020. The study suggests that China will, indeed, require rapidly growing imports of oil, coal, and gas. This growth is not so sensitive to the rate of economic growth as to increases in motorization. It can be offset, but probably only in small part, by increasing domestic energy production or by improvements in the efficiency of use, particularly in the production of electric power. (author)

  19. Forecasting of the incorporated energy in the final demand of the brazilian economy in 2005

    International Nuclear Information System (INIS)

    Cunha, Marcelo Pereira da; Pereira, Jose Tomaz Vieira

    2008-01-01

    This work presents the application of a methodology for evaluation of the primary energy incorporated by the productive sectors of a economy at the final demand - using of a income-product mode. A methodology is applied in the evaluation of the energy incorporated to 25 sectors of the brazilian economy, by using the the data available in the national counts (IBGE - 2007), and the National Energy Balance for the year 2005 (EPE - 2007). For each sector, the results are presented in terms of the primary energy incorporated (in petroleum equivalent tons per R$ 1,000), of the participation of renewable energy, and the total primary energy distribution for the offered products by the 25 sectors to be consumption by the final demand. Among some interesting results in terms of final demand, it is highlighted the presence of 96.5% of renewable primary energy for the sector of alcohol, and 5.3% for the sector of petroleum refining products sector. In terms of the total energy distribution,the petroleum refining and coke sector were the most significant contribution to the incorporation of primary energy, presenting 16.1% of the total ahead of foods and beverages which presents 12.1%. Related to the final demand components, families consumption was responsible by the 57.7% of the total, the exports with 25.3%, the gross capital formation (investments and stock variations) with 11.3%, and the govern consumption wit 5.7%

  20. A forecast of typhoid conjugate vaccine introduction and demand in typhoid endemic low- and middle-income countries to support vaccine introduction policy and decisions.

    Science.gov (United States)

    Mogasale, Vittal; Ramani, Enusa; Park, Il Yeon; Lee, Jung Seok

    2017-09-02

    A Typhoid Conjugate Vaccine (TCV) is expected to acquire WHO prequalification soon, which will pave the way for its use in many low- and middle-income countries where typhoid fever is endemic. Thus it is critical to forecast future vaccine demand to ensure supply meets demand, and to facilitate vaccine policy and introduction planning. We forecasted introduction dates for countries based on specific criteria and estimated vaccine demand by year for defined vaccination strategies in 2 scenarios: rapid vaccine introduction and slow vaccine introduction. In the rapid introduction scenario, we forecasted 17 countries and India introducing TCV in the first 5 y of the vaccine's availability while in the slow introduction scenario we forecasted 4 countries and India introducing TCV in the same time period. If the vaccine is targeting infants in high-risk populations as a routine single dose, the vaccine demand peaks around 40 million doses per year under the rapid introduction scenario. Similarly, if the vaccine is targeting infants in the general population as a routine single dose, the vaccine demand increases to 160 million doses per year under the rapid introduction scenario. The demand forecast projected here is an upper bound estimate of vaccine demand, where actual demand depends on various factors such as country priorities, actual vaccine introduction, vaccination strategies, Gavi financing, costs, and overall product profile. Considering the potential role of TCV in typhoid control globally; manufacturers, policymakers, donors and financing bodies should work together to ensure vaccine access through sufficient production capacity, early WHO prequalification of the vaccine, continued Gavi financing and supportive policy.

  1. Spatial resolution of precipitation and radiation: the effect on regional crop yield forecasts

    NARCIS (Netherlands)

    Wit, de A.J.W.; Boogaard, H.L.; Diepen, van C.A.

    2005-01-01

    This paper explores the effect of uncertainty in precipitation and radiation on crop simulation results at local (50 × 50 km grids) and regional scale (NUTS1 regions) and on the crop yield forecasts for Germany and France. Two experiments were carried out where crop yields for winter-wheat and grain

  2. Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey

    International Nuclear Information System (INIS)

    Günay, M. Erdem

    2016-01-01

    In this work, the annual gross electricity demand of Turkey was modeled by multiple linear regression and artificial neural networks as a function population, gross domestic product per capita, inflation percentage, unemployment percentage, average summer temperature and average winter temperature. Among these, the unemployment percentage and the average winter temperature were found to be insignificant to determine the demand for the years between 1975 and 2013. Next, the future values of the statistically significant variables were predicted by time series ANN models, and these were simulated in a multilayer perceptron ANN model to forecast the future annual electricity demand. The results were validated with a very high accuracy for the years that the electricity demand was known (2007–2013), and they were also superior to the official predictions (done by Ministry of Energy and Natural Resources of Turkey). The model was then used to forecast the annual gross electricity demand for the future years, and it was found that, the demand will be doubled reaching about 460 TW h in the year 2028. Finally, it was concluded that the approach applied in this work can easily be implemented for other countries to make accurate predictions for the future. - Highlights: • Electricity demand of Turkey increased from 15.6 to 246.4 TW h in 1975–2013 period. • Population, GDP per capita, inflation and average summer temperature influence demand. • Future values of descriptor variables can be predicted by time series ANN models. • ANN model simulated by the predicted values of descriptors can forecast the demand. • Demand is forecasted to be doubled reaching about 460 TW h in the year 2028.

  3. Using Climate Regionalization to Understand Climate Forecast System Version 2 (CFSv2) Precipitation Performance for the Conterminous United States (CONUS)

    Science.gov (United States)

    Regonda, Satish K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Rodell, Matthew

    2016-01-01

    Dynamically based seasonal forecasts are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low-forecast skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large-scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate Forecast SystemVersion 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month forecasts capture the general spatial character of warm season precipitation variability but that forecast regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional-scale forecast in place of the local grid cell prediction.

  4. A broad model for demand forecasting of gasoline and fuel alcohol; Um modelo abrangente para a projecao das demandas de gasolina e alcool carburante

    Energy Technology Data Exchange (ETDEWEB)

    Buonfiglio, Antonio [PETROBRAS, Paulinia, SP (Brazil). Dept. Industrial; Bajay, Sergio Valdir [Universidade Estadual de Campinas, SP (Brazil). Faculdade de Engenharia Mecanica

    1992-12-31

    Formulating a broad, mixed: econometric/end-use, demand forecasting model for gasoline and fuel alcohol is the main objective of this work. In the model, the gasoline and hydrated alcohol demands are calculated as the corresponding products if their fleet by the average car mileage, divided by the average specific mileage. Several simulations with the proposed forecasting model are carried out, within the context of alternative scenarios for the development of these competing fuels in the Brazilian market. (author) 4 refs., 1 fig., 3 tabs.

  5. Modeling and Forecasting the Impact of Major Technological and Infrastructural Changes on Travel Demand

    OpenAIRE

    El Zarwi, Feras

    2017-01-01

    The transportation system is undergoing major technological and infrastructural changes, such as the introduction of autonomous vehicles, high speed rail, carsharing, ridesharing, flying cars, drones, and other app-driven on-demand services. While the changes are imminent, the impact on travel behavior is uncertain, as is the role of policy in shaping the future. Literature shows that even under the most optimistic scenarios, society’s environmental goals cannot be met by technology, operatio...

  6. DESIGNING A SUPPLY CHAIN MODEL WITH CONSIDERATION DEMAND FORECASTING AND INFORMATION SHARING

    Directory of Open Access Journals (Sweden)

    S.M.T. Fatemi Ghomi

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: In traditional supply chain inventory management, orders are the only information firms exchange, but information technology now allows firms to share demand and inventory data quickly and inexpensively. To have an integrated plan, a manufacturer not only needs to know demand information from its customers but also supply information from its suppliers. In this paper, information flow is incorporated in a three-echelon supply chain model. Also to decrease the risk of the supply chain system, the customers’ demands are predicted first and this prediction is then used as an input to the supply chain model. In this paper a proposed evolving fuzzy predictor model will be used to predict the customers’ demands. For solving the supply chain model, a hybrid heuristic combining tabu search with simulated annealing sharing the same tabu list is developed.

    AFRIKAANSE OPSOMMING: In tradisionele voorsieningskettingvoorraadbestuur verteenwoordig bestellings die enigste vorm van van inligting wat deur ondernemings uitgeruil word. Inligtingstegnologie laat ondernemings egter deesdae toe om vraag- en voorraadata vinnig en goedkoop uit te ruil. Om 'n geïntegreerde plan te hê, het 'n vervaardiger nie alleen aanvraaginligting nodig van sy klante nie, maar ook aanbodinligting van sy leweransiers. In hierdie artikel word inligtingvloei geinkorporeer in 'n drie-vlakvoorsieningskettingmodel. Voorts, om die risiko in die voorsieningskettingmodel te verminder, word die klante se aanvraag eers vooruitgeskat en die vooruitskatting word dan gebruik as 'n inset tot die model. Hierdie artikel gebruik 'n groeiende wasige vooruitskattingsmodel om die klantebehoeftes voor uit te skat. Om die model op te los, word 'n hibriede heuristiese metode gekombineer met 'n "tabu"-soektog gebruik.

  7. Forecasting supply and demand in nursing professions: impacts of occupational flexibility and employment structure in Germany

    OpenAIRE

    Maier, Tobias; Afentakis, Anja

    2013-01-01

    Background In light of Germany's ageing society, demand for nursing professionals is expected to increase in the coming years. This will pose a challenge for policy makers to increase the supply of nursing professionals. Methodology To portray the different possible developments in the supply of nursing professionals, we projected the supply of formally trained nurses and the potential supply of persons who are able to work in a nursing profession. This potential supply of nursing professiona...

  8. A Simple Forecasting Model Linking Macroeconomic Policy to Industrial Employment Demand.

    Science.gov (United States)

    Malley, James R.; Hady, Thomas F.

    A study detailed further a model linking monetary and fiscal policy to industrial employment in metropolitan and nonmetropolitan areas of four United States regions. The model was used to simulate the impacts on area and regional employment of three events in the economy: changing real gross national product (GNP) via monetary policy, holding the…

  9. Electrical demand forecast in two different scenarios of socio-economic development

    International Nuclear Information System (INIS)

    Goni, M.R.

    1996-01-01

    A projection of electrical demand for two different scenarios is presented in the study. The study period is 1993-2010 and 1993 has been taken as base year. In this planning study MAED program was used as well as all available information from INDEC (National Statistical Body), CAMMESA (Electrical Market Company) and Ministery of Economy. The results in the base year achieved an accuracy higher than 98%. The scenarios described two different rates of growth and electrical penetration in energy uses. (author). 3 refs., 9 figs., 2 tabs

  10. Forecasting supply and demand in nursing professions: impacts of occupational flexibility and employment structure in Germany

    Science.gov (United States)

    2013-01-01

    Background In light of Germany's ageing society, demand for nursing professionals is expected to increase in the coming years. This will pose a challenge for policy makers to increase the supply of nursing professionals. Methodology To portray the different possible developments in the supply of nursing professionals, we projected the supply of formally trained nurses and the potential supply of persons who are able to work in a nursing profession. This potential supply of nursing professionals was calculated on the basis of empirical information on occupational mobility provided by the German Microcensus 2005 (Labour Force Survey). We also calculated how the supply of full-time equivalents (FTEs) will develop if current employment structures develop in the direction of employment behaviour in nursing professions in eastern and western Germany. We then compared these different supply scenarios with two demand projections ('status quo' and 'compression of morbidity' scenarios) from Germany's Federal Statistical Office. Results Our results show that, even as early as 2005, meeting demand for FTEs in nursing professions was not arithmetically possible when only persons with formal qualification in a nursing profession were taken into account on the supply side. When additional semi-skilled nursing professionals are included in the calculation, a shortage of labour in nursing professions can be expected in 2018 when the employment structure for all nursing professionals remains the same as the employment structure seen in Germany in 2005 (demand: 'status quo scenario'). Furthermore, given an employment structure as in eastern Germany, where more nursing professionals work on a full-time basis with longer working hours, a theoretical shortage of nursing professionals could be delayed until 2024. Conclusions Our analysis of occupational flexibility in the nursing field indicates that additional potential supply could be generated by especially training more young people

  11. DETERMINANTS OF DEMAND FOR AGRICULTURAL CREDIT IN NICARAGUA (1996-2009 AND FORECAST (2010-2012

    Directory of Open Access Journals (Sweden)

    Iván Salvador Romero A.

    2014-11-01

    Full Text Available Nicaragua has a small economy with a high degree of trade openness, structural deficit in its balance of trade and services, with a low supply of exportable value added agro-based growth model and therefore vulnerable to exogenous shocks altering its relations  of terms of trade. The time series analyzed in this study shows that despite the nature of their agro-export model are the secondary and tertiary sectors and not the primary, the ones driving the growth of the Nicaraguan economy; We will explain the observed, considering what determines the demand for agricultural credit in this economy.

  12. Forecasting supply and demand in nursing professions: impacts of occupational flexibility and employment structure in Germany.

    Science.gov (United States)

    Maier, Tobias; Afentakis, Anja

    2013-06-05

    In light of Germany's ageing society, demand for nursing professionals is expected to increase in the coming years. This will pose a challenge for policy makers to increase the supply of nursing professionals. To portray the different possible developments in the supply of nursing professionals, we projected the supply of formally trained nurses and the potential supply of persons who are able to work in a nursing profession. This potential supply of nursing professionals was calculated on the basis of empirical information on occupational mobility provided by the German Microcensus 2005 (Labour Force Survey). We also calculated how the supply of full-time equivalents (FTEs) will develop if current employment structures develop in the direction of employment behaviour in nursing professions in eastern and western Germany. We then compared these different supply scenarios with two demand projections ('status quo' and 'compression of morbidity' scenarios) from Germany's Federal Statistical Office. Our results show that, even as early as 2005, meeting demand for FTEs in nursing professions was not arithmetically possible when only persons with formal qualification in a nursing profession were taken into account on the supply side. When additional semi-skilled nursing professionals are included in the calculation, a shortage of labour in nursing professions can be expected in 2018 when the employment structure for all nursing professionals remains the same as the employment structure seen in Germany in 2005 (demand: 'status quo scenario'). Furthermore, given an employment structure as in eastern Germany, where more nursing professionals work on a full-time basis with longer working hours, a theoretical shortage of nursing professionals could be delayed until 2024. Our analysis of occupational flexibility in the nursing field indicates that additional potential supply could be generated by especially training more young people for a nursing profession as they tend to

  13. Forecasting the daily electricity consumption in the Moscow region using artificial neural networks

    Science.gov (United States)

    Ivanov, V. V.; Kryanev, A. V.; Osetrov, E. S.

    2017-07-01

    In [1] we demonstrated the possibility in principle for short-term forecasting of daily volumes of passenger traffic in the Moscow metro with the help of artificial neural networks. During training and predicting, a set of the factors that affect the daily passenger traffic in the subway is passed to the input of the neural network. One of these factors is the daily power consumption in the Moscow region. Therefore, to predict the volume of the passenger traffic in the subway, we must first to solve the problem of forecasting the daily energy consumption in the Moscow region.

  14. The Santos Basin Ocean Observing System: From R&D to Operational Regional Forecasts

    Science.gov (United States)

    Da Rocha Fragoso, M.; Moore, A. M.; dos Santos, F. A.; Marques Da Cruz, L.; Carvalho, G. V.; Soares, F.

    2016-02-01

    Santos Basin is located on the Southwestern Brazilian Ocean Basin and comprises the main offshore oil reserves of Brazil. The exploration and production activities on its ocean are growing in accelerated pace, which means that oil spill contingency and search & rescue operations are likely to be more frequent. Therefore, ocean current reliable nowcasts and forecasts has become even more important for this region. The Santos Basin Ocean Observing System was designed as an R&D project and its main objective was to establish and maintain a systematic oceanographic data collection for this region in order to study its ocean dynamics and improve regional ocean forecast through data assimilation. In the first three years of the project surface drifters, profiling floats and gliders were deployed to measure and monitor mainly the Brazil Current Western Boundary System, a highly unstable baroclinic current system, that present several meanders and mesoscale eddies activities. Throughout the development of the project, the team involved was able to learn how to operate the equipment, treat the collected data and use it to assimilate on the Regional Ocean Modeling System (ROMS). After performing a one-year 4DVAR assimilation cycle (Fragoso et al., 2015) in which the forecasting skill was assessed, the system was considered mature enough to start producing ocean circulation forecasts for Santos Basin. It is the first time in Brazil that a regional ocean model using a 4DVAR data assimilation scheme was used to produce high resolution operational ocean current forecasts. This paper describes all the components of this forecasting system, its main results and discoveries with special focus on the Brazil Current System Transport and mesocale eddies dynamics and statistics.

  15. A review of operational, regional-scale, chemical weather forecasting models in Europe

    Directory of Open Access Journals (Sweden)

    J. Kukkonen

    2012-01-01

    Full Text Available Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

  16. Application of Regional Drought and Crop Yield Information System to enhance drought monitoring and forecasting in Lower Mekong region

    Science.gov (United States)

    Jayasinghe, S.; Dutta, R.; Basnayake, S. B.; Granger, S. L.; Andreadis, K. M.; Das, N.; Markert, K. N.; Cutter, P. G.; Towashiraporn, P.; Anderson, E.

    2017-12-01

    The Lower Mekong Region has been experiencing frequent and prolonged droughts resulting in severe damage to agricultural production leading to food insecurity and impacts on livelihoods of the farming communities. Climate variability further complicates the situation by making drought harder to forecast. The Regional Drought and Crop Yield Information System (RDCYIS), developed by SERVIR-Mekong, helps decision makers to take effective measures through monitoring, analyzing and forecasting of drought conditions and providing early warnings to farmers to make adjustments to cropping calendars. The RDCYIS is built on regionally calibrated Regional Hydrologic Extreme Assessment System (RHEAS) framework that integrates the Variable Infiltration Capacity (VIC) and Decision Support System for Agro-technology Transfer (DSSAT) models, allowing both nowcast and forecast of drought. The RHEAS allows ingestion of numerus freely available earth observation and ground observation data to generate and customize drought related indices, variables and crop yield information for better decision making. The Lower Mekong region has experienced severe drought in 2016 encompassing the region's worst drought in 90 years. This paper presents the simulation of the 2016 drought event using RDCYIS based on its hindcast and forecast capabilities. The regionally calibrated RDCYIS can help capture salient features of drought through a variety of drought indices, soil variables, energy balance variables and water balance variables. The RDCYIS is capable of assimilating soil moisture data from different satellite products and perform ensemble runs to further reduce the uncertainty of it outputs. The calibrated results have correlation coefficient around 0.73 and NSE between 0.4-0.5. Based on the acceptable results of the retrospective runs, the system has the potential to generate reliable drought monitoring and forecasting information to improve decision-makings at operational, technological and

  17. MAG4 versus alternative techniques for forecasting active region flare productivity

    Science.gov (United States)

    Falconer, David A; Moore, Ronald L; Barghouty, Abdulnasser F; Khazanov, Igor

    2014-01-01

    MAG4 is a technique of forecasting an active region's rate of production of major flares in the coming few days from a free magnetic energy proxy. We present a statistical method of measuring the difference in performance between MAG4 and comparable alternative techniques that forecast an active region's major-flare productivity from alternative observed aspects of the active region. We demonstrate the method by measuring the difference in performance between the “Present MAG4” technique and each of three alternative techniques, called “McIntosh Active-Region Class,” “Total Magnetic Flux,” and “Next MAG4.” We do this by using (1) the MAG4 database of magnetograms and major flare histories of sunspot active regions, (2) the NOAA table of the major-flare productivity of each of 60 McIntosh active-region classes of sunspot active regions, and (3) five technique performance metrics (Heidke Skill Score, True Skill Score, Percent Correct, Probability of Detection, and False Alarm Rate) evaluated from 2000 random two-by-two contingency tables obtained from the databases. We find that (1) Present MAG4 far outperforms both McIntosh Active-Region Class and Total Magnetic Flux, (2) Next MAG4 significantly outperforms Present MAG4, (3) the performance of Next MAG4 is insensitive to the forward and backward temporal windows used, in the range of one to a few days, and (4) forecasting from the free-energy proxy in combination with either any broad category of McIntosh active-region classes or any Mount Wilson active-region class gives no significant performance improvement over forecasting from the free-energy proxy alone (Present MAG4). Key Points Quantitative comparison of performance of pairs of forecasting techniques Next MAG4 forecasts major flares more accurately than Present MAG4 Present MAG4 forecast outperforms McIntosh AR Class and total magnetic flux PMID:26213517

  18. Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

    Science.gov (United States)

    Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

    2017-12-01

    The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

  19. Forecasting of the radioactive material transport demand for the Brazilian Nuclear Program and the security aspects

    International Nuclear Information System (INIS)

    Meldonian, Nelson Leon

    1979-01-01

    In the nuclear fuel cycle, a lot of radioactive materials are produced. These radioactive materials must be transported in order to promote the integration of the fuel cycle units. Considerations about the transport characteristics of radioactive material were made for each section of the fuel cycle. These considerations were based on the experience of several countries and in accordance with the International Atomic Energy Agency regulations. A prediction of transport demands for the Brazilian Nuclear Program until year 2.010 was made. The prediction refers mainly to the quantity of radioactive material produced in each section of the cycle the quantity of vehicles needed for the transport of these materials. Several safety aspects were considered specially, the accidents predictions for years 2.000 and 2.010. The accident probability in Brazilian railroads and highways was compared with that of the USA. (author)

  20. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey

    International Nuclear Information System (INIS)

    Akay, Diyar; Atak, Mehmet

    2007-01-01

    The need for energy supply, especially for electricity, has been increasing in the last two decades in Turkey. In addition, owing to the uncertain economic structure of the country, electricity consumption has a chaotic and nonlinear trend. Hence, electricity configuration planning and estimation has been the most critical issue of active concern for Turkey. The Turkish Ministry of Energy and Natural Resources (MENR) has officially carried out energy planning studies using the Model of Analysis of the Energy Demand (MAED). In this paper, Grey prediction with rolling mechanism (GPRM) approach is proposed to predict the Turkey's total and industrial electricity consumption. GPRM approach is used because of high prediction accuracy, applicability in the case of limited data situations and requirement of little computational effort. Results show that proposed approach estimates more accurate results than the results of MAED, and have explicit advantages over extant studies. Future projections have also been done for total and industrial sector, respectively

  1. Incorporating regional growth into forecasts of greenhouse gas emissions from project-level residential and commercial development

    International Nuclear Information System (INIS)

    Rowangould, Dana; Eldridge, Melody; Niemeier, Deb

    2013-01-01

    To better understand the greenhouse gas (GHG) implications of land use planning decisions, regional planning organizations have developed tools to forecast the emissions from project-level residential and commercial development. This paper reviews the state of GHG emissions forecasting methods for project-level development. We argue that when forecasting changes in regional emissions it is important to make explicit what is assumed about a project′s effect on the population of residents and businesses in the region. We present five regional growth assumptions capturing the range of ways that project-level development might influence (i) construction and occupancy of similar developments elsewhere in a region and (ii) relocation of the initial activities that occur on-site before the project is built. We show that current forecasting tools inconsistently address the latter when they are interpreted as forecasted changes in regional emissions. Using a case study in Yolo County, California we demonstrate that forecasted changes in regional emissions are greatly affected by the regional growth assumption. In the absence of information about which regional growth assumption is accurate, we provide guidelines for selection of a conservative regional growth assumption. - Highlights: • Current tools inconsistently forecast GHG emissions from project-level development. • We outline five assumptions about how projects may affect regional growth. • Our assumptions capture a range of economic and population effects of projects. • Our case study shows that growth assumptions greatly affect regional GHG estimates. • We provide guidelines for selecting a conservative regional growth assumption

  2. Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

    Science.gov (United States)

    Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

    1983-09-01

    Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

  3. Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

    Science.gov (United States)

    Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

    2010-05-01

    The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

  4. Classical gas: Hearty prices, robust demand combine to pump breezy optimism through 2005 forecasts

    International Nuclear Information System (INIS)

    Lunan, D.

    2005-01-01

    The outlook for natural gas in 2005 is said to be a watershed year, with a lengthy list of developments that could have significant effect on the industry for many years to come. In light of continuing high demand and static supply prospects, prices will have to continue to be high in order to ensure the necessary infrastructure investments to keep gas flowing from multiple sources to the consumer. It is predicted that against the backdrop of robust prices several supply initiatives will continue to advance rapidly in 2005, such as the $7 billion Mackenzie Gas Project on which public hearings are expected to start this summer, along with regulatory clarity about the $20 billion Alaska Highway Natural Gas Pipeline Project to move North Slope gas to southern markets. Drilling of new gas wells will continue to approach or even surpass 18,000 new wells, with an increasing number of these being coal-bed methane wells. Despite high level drilling activity, supply is expected to grow only about 400 MMcf per day. Greater supply increments are expected through continued LNG terminal development, although plans for new LNG terminal development have been met with stiff resistance from local residents both in Canada and the United States. Imports of liquefied natural gas into the United States slowed dramatically in 2004 under the severe short-term downward pressure on natural gas prices, nevertheless, these imports are expected to rebound to new record highs in 2005. Capacity is expected to climb from about 2.55 Bcf per day in 2004 to as much as 6.4 Bcf per day by late 2007. At least one Canadian import facility, Anadarko's one Bcf per day Bear Head terminal on Nova Scotia's Strait of Canso, is expected to become operational by late 2007 or early 2008. 6 photos

  5. Modeling and Forecast Biological Oxygen Demand (BOD using Combination Support Vector Machine with Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Abazar Solgi

    2017-06-01

    Full Text Available Introduction: Chemical pollution of surface water is one of the serious issues that threaten the quality of water. This would be more important when the surface waters used for human drinking supply. One of the key parameters used to measure water pollution is BOD. Because many variables affect the water quality parameters and a complex nonlinear relationship between them is established conventional methods can not solve the problem of quality management of water resources. For years, the Artificial Intelligence methods were used for prediction of nonlinear time series and a good performance of them has been reported. Recently, the wavelet transform that is a signal processing method, has shown good performance in hydrological modeling and is widely used. Extensive research has been globally provided in use of Artificial Neural Network and Adaptive Neural Fuzzy Inference System models to forecast the BOD. But support vector machine has not yet been extensively studied. For this purpose, in this study the ability of support vector machine to predict the monthly BOD parameter based on the available data, temperature, river flow, DO and BOD was evaluated. Materials and Methods: SVM was introduced in 1992 by Vapnik that was a Russian mathematician. This method has been built based on the statistical learning theory. In recent years the use of SVM, is highly taken into consideration. SVM was used in applications such as handwriting recognition, face recognition and has good results. Linear SVM is simplest type of SVM, consists of a hyperplane that dataset of positive and negative is separated with maximum distance. The suitable separator has maximum distance from every one of two dataset. So about this machine that its output groups label (here -1 to +1, the aim is to obtain the maximum distance between categories. This is interpreted to have a maximum margin. Wavelet transform is one of methods in the mathematical science that its main idea was

  6. Alternative supply specifications and estimates of regional supply and demand for stumpage.

    Science.gov (United States)

    Kent P. Connaughton; David H. Jackson; Gerard A. Majerus

    1988-01-01

    Four plausible sets of stumpage supply and demand equations were developed and estimated; the demand equation was the same for each set, although the supply equation differed. The supply specifications varied from the model of regional excess demand in which National Forest harvest levels were assumed fixed to a more realistic model in which the harvest on the National...

  7. Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

    Directory of Open Access Journals (Sweden)

    Adeshina Y. Alani

    2017-10-01

    Full Text Available Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.

  8. A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

    Directory of Open Access Journals (Sweden)

    Jia Ning

    2017-11-01

    Full Text Available The uncertainty of wind power results in wind power forecasting errors (WPFE which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC units to smooth the system’s imbalance, considering the real-time DR potential (DRP and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC, water heater (WH, electric vehicle (EV loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers’ comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts.

  9. Regional forecasting with global atmospheric models; Final report

    Energy Technology Data Exchange (ETDEWEB)

    Crowley, T.J.; Smith, N.R. [Applied Research Corp., College Station, TX (United States)

    1994-05-01

    The purpose of the project was to conduct model simulations for past and future climate change with respect to the proposed Yucca Mtn. repository. The authors report on three main topics, one of which is boundary conditions for paleo-hindcast studies. These conditions are necessary for the conduction of three to four model simulations. The boundary conditions have been prepared for future runs. The second topic is (a) comparing the atmospheric general circulation model (GCM) with observations and other GCMs; and (b) development of a better precipitation data base for the Yucca Mtn. region for comparisons with models. These tasks have been completed. The third topic is preliminary assessments of future climate change. Energy balance model (EBM) simulations suggest that the greenhouse effect will likely dominate climate change at Yucca Mtn. for the next 10,000 years. The EBM study should improve rational choice of GCM CO{sub 2} scenarios for future climate change.

  10. Changing Professional Demands in Sustainable Regional Development: A Curriculum Design Process to meet Transboundary Competence

    NARCIS (Netherlands)

    Lansu, Angelique; Boon, Jo; Sloep, Peter; van Dam-Mieras, Rietje

    2012-01-01

    Lansu, A., Boon, J., Sloep, P. B., & Van Dam-Mieras, R. (Accepted). Changing Professional Demands in Sustainable Regional Development: A Curriculum Design Process to meet Transboundary Competence. Journal of Cleaner Production. [Special Issue: Learning for Sustainable Development in Regional

  11. Regional Energy Demand Responses To Climate Change. Methodology And Application To The Commonwealth Of Massachusetts

    International Nuclear Information System (INIS)

    Amato, A.D.; Ruth, M.; Kirshen, P.; Horwitz, J.

    2005-01-01

    Climate is a major determinant of energy demand. Changes in climate may alter energy demand as well as energy demand patterns. This study investigates the implications of climate change for energy demand under the hypothesis that impacts are scale dependent due to region-specific climatic variables, infrastructure, socioeconomic, and energy use profiles. In this analysis we explore regional energy demand responses to climate change by assessing temperature-sensitive energy demand in the Commonwealth of Massachusetts. The study employs a two-step estimation and modeling procedure. The first step evaluates the historic temperature sensitivity of residential and commercial demand for electricity and heating fuels, using a degree-day methodology. We find that when controlling for socioeconomic factors, degree-day variables have significant explanatory power in describing historic changes in residential and commercial energy demands. In the second step, we assess potential future energy demand responses to scenarios of climate change. Model results are based on alternative climate scenarios that were specifically derived for the region on the basis of local climatological data, coupled with regional information from available global climate models. We find notable changes with respect to overall energy consumption by, and energy mix of the residential and commercial sectors in the region. On the basis of our findings, we identify several methodological issues relevant to the development of climate change impact assessments of energy demand

  12. Regional Energy Demand Responses To Climate Change. Methodology And Application To The Commonwealth Of Massachusetts

    Energy Technology Data Exchange (ETDEWEB)

    Amato, A.D.; Ruth, M. [Environmental Policy Program, School of Public Policy, University of Maryland, 3139 Van Munching Hall, College Park, MD (United States); Kirshen, P. [Department of Civil and Environmental Engineering, Tufts University, Anderson Hall, Medford, MA (United States); Horwitz, J. [Climatological Database Consultant, Binary Systems Software, Newton, MA (United States)

    2005-07-01

    Climate is a major determinant of energy demand. Changes in climate may alter energy demand as well as energy demand patterns. This study investigates the implications of climate change for energy demand under the hypothesis that impacts are scale dependent due to region-specific climatic variables, infrastructure, socioeconomic, and energy use profiles. In this analysis we explore regional energy demand responses to climate change by assessing temperature-sensitive energy demand in the Commonwealth of Massachusetts. The study employs a two-step estimation and modeling procedure. The first step evaluates the historic temperature sensitivity of residential and commercial demand for electricity and heating fuels, using a degree-day methodology. We find that when controlling for socioeconomic factors, degree-day variables have significant explanatory power in describing historic changes in residential and commercial energy demands. In the second step, we assess potential future energy demand responses to scenarios of climate change. Model results are based on alternative climate scenarios that were specifically derived for the region on the basis of local climatological data, coupled with regional information from available global climate models. We find notable changes with respect to overall energy consumption by, and energy mix of the residential and commercial sectors in the region. On the basis of our findings, we identify several methodological issues relevant to the development of climate change impact assessments of energy demand.

  13. Derivation of travel demand forecasting models for low population areas: the case of Port Said Governorate, North East Egypt

    Directory of Open Access Journals (Sweden)

    Ahmed Mohamed Semeida

    2014-06-01

    Full Text Available In the last decades, there has been substantial development in modeling techniques of travel demand estimation. For low population areas the external trip estimation is important but usually neglected in travel demand modeling process. In Egypt, the researches in this field are scarce due to lack of data. Accordingly, this paper aims to identify and estimate the main variables that affect the travel demand in low population areas, and to develop models to predict them. The study focused on the Port Said Governorate in North East Egypt. A special questionnaire had been prepared in 2010 depending on interviews of passengers at basic taxi terminals in Port Said. And 2211 filled questionnaires were offering for research. To analyze the data, two modeling procedures were used. One is the multiple linear regression and the other is the generalized linear modeling (GLM applying normal distributions. It is found that GLM procedure offers more suitable and accurate approach than the linear regression for developing number of trips. The final demand models have statistics within the acceptable regions and, also, they are conceptually reasonable. These results are so important for Egyptian highway authorities to improve the efficiency of highway transportation system in Egypt.

  14. Evaluation of Optimized WRF Precipitation Forecast over a Complex Topography Region during Flood Season

    Directory of Open Access Journals (Sweden)

    Yuan Li

    2016-11-01

    Full Text Available In recent years, the Weather Research and Forecast (WRF model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic downscaling ability of the WRF model. In this study, the influence of these factors has been analyzed in precipitation forecasting for the Xijiang Basin, southern China—a region with complex topography. The results indicate that higher horizontal resolutions always result in higher Critical Success Indexes (CSI, but higher biases as well. Meanwhile, the precipitation forecast skills are also influenced by the combination of microphysics parameterization scheme and cumulus convective parameterization scheme. On the basis of these results, an optimized configuration of the WRF model is built in which the horizontal resolution is 10 km, the microphysics parameterization is the Lin scheme, and the cumulus convective parameterization is the Betts–Miller–Janjic scheme. This configuration is then evaluated by simulating the daily weather during the 2013–2014 flood season. The high Critical Success Index scores and low biases at various thresholds and lead times confirm the high accuracy of the optimized WRF model configuration for Xijiang Basin. However, the performance of the WRF model varies from different sub-basins due to the complexity of the mesoscale convective system (MCS over this region.

  15. A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

    Directory of Open Access Journals (Sweden)

    S. Zhang

    2018-03-01

    Full Text Available Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

  16. A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

    Science.gov (United States)

    Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

    2018-03-01

    Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

  17. Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models

    International Nuclear Information System (INIS)

    Truong, Nguyen-Vu; Wang, Liuping; Wong, Peter K.C.

    2008-01-01

    Power demand forecasting is of vital importance to the management and planning of power system operations which include generation, transmission, distribution, as well as system's security analysis and economic pricing processes. This paper concerns the modeling and short-term forecast of daily peak power demand in the state of Victoria, Australia. In this study, a two-dimensional wavelet based state dependent parameter (SDP) modelling approach is used to produce a compact mathematical model for this complex nonlinear dynamic system. In this approach, a nonlinear system is expressed by a set of linear regressive input and output terms (state variables) multiplied by the respective state dependent parameters that carry the nonlinearities in the form of 2-D wavelet series expansions. This model is identified based on historical data, descriptively representing the relationship and interaction between various components which affect the peak power demand of a certain day. The identified model has been used to forecast daily peak power demand in the state of Victoria, Australia in the time period from the 9th of August 2007 to the 24th of August 2007. With a MAPE (mean absolute prediction error) of 1.9%, it has clearly implied the effectiveness of the identified model. (author)

  18. Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

    Science.gov (United States)

    Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

    2010-01-01

    Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation

  19. Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.

    2009-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

  20. Water availability and demand in the development regions of South Africa

    Directory of Open Access Journals (Sweden)

    A. B. de Villiers

    1988-03-01

    Full Text Available The availability of water data in the development regions is at present insufficient. This is due to the fact that water supply and demand is calculated for the physical drainage regions (watersheds, while the development regions do not correspond with the drainage regions. The necessary calculations can accordingly presently not be made. In this paper this problem is addressed.

  1. The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting

    Science.gov (United States)

    Kurzrock, Frederik; Cros, Sylvain; Chane-Ming, Fabrice; Potthast, Roland; Linguet, Laurent; Sébastien, Nicolas

    2016-04-01

    As an intermittent energy source, the injection of solar power into electricity grids requires irradiance forecasting in order to ensure grid stability. On time scales of more than six hours ahead, numerical weather prediction (NWP) is recognized as the most appropriate solution. However, the current representation of clouds in NWP models is not sufficiently precise for an accurate forecast of solar irradiance at ground level. Dynamical downscaling does not necessarily increase the quality of irradiance forecasts. Furthermore, incorrectly simulated cloud evolution is often the cause of inaccurate atmospheric analyses. In non-interconnected tropical areas, the large amplitudes of solar irradiance variability provide abundant solar yield but present significant problems for grid safety. Irradiance forecasting is particularly important for solar power stakeholders in these regions where PV electricity penetration is increasing. At the same time, NWP is markedly more challenging in tropic areas than in mid-latitudes due to the special characteristics of tropical homogeneous convective air masses. Numerous data assimilation methods and strategies have evolved and been applied to a large variety of global and regional NWP models in the recent decades. Assimilating data from geostationary meteorological satellites is an appropriate approach. Indeed, models converting radiances measured by satellites into cloud properties already exist. Moreover, data are available at high temporal frequencies, which enable a pertinent cloud cover evolution modelling for solar energy forecasts. In this work, we present a survey of different approaches which aim at improving cloud cover forecasts using the assimilation of geostationary meteorological satellite data into regional NWP models. Various approaches have been applied to a variety of models and satellites and in different regions of the world. Current methods focus on the assimilation of cloud-top information, derived from infrared

  2. Demand forecasts at national and EU level on a computer-based model taking usage costs into account

    DEFF Research Database (Denmark)

    Passamonti, Lucia; Falch, Morten; Björksten, Margareta

    1997-01-01

    The objective of this deliverable is to forecast the residential spending on selected multimedia services such as Tele-entertainment, VOD, AOD, Networked games, Teleshopping and Teleworking.......The objective of this deliverable is to forecast the residential spending on selected multimedia services such as Tele-entertainment, VOD, AOD, Networked games, Teleshopping and Teleworking....

  3. a system approach to the long term forecasting of the climat data in baikal region

    Science.gov (United States)

    Abasov, N.; Berezhnykh, T.

    2003-04-01

    The Angara river running from Baikal with a cascade of hydropower plants built on it plays a peculiar role in economy of the region. With view of high variability of water inflow into the rivers and lakes (long-term low water periods and catastrophic floods) that is due to climatic peculiarities of the water resource formation, a long-term forecasting is developed and applied for risk decreasing at hydropower plants. Methodology and methods of long-term forecasting of natural-climatic processes employs some ideas of the research schools by Academician I.P.Druzhinin and Prof. A.P.Reznikhov and consists in detailed investigation of cause-effect relations, finding out physical analogs and their application to formalized methods of long-term forecasting. They are divided into qualitative (background method; method of analogs based on solar activity), probabilistic and approximative methods (analog-similarity relations; discrete-continuous model). These forecasting methods have been implemented in the form of analytical aids of the information-forecasting software "GIPSAR" that provides for some elements of artificial intelligence. Background forecasts of the runoff of the Ob, the Yenisei, the Angara Rivers in the south of Siberia are based on space-time regularities that were revealed on taking account of the phase shifts in occurrence of secular maxima and minima on integral-difference curves of many-year hydrological processes in objects compared. Solar activity plays an essential role in investigations of global variations of climatic processes. Its consideration in the method of superimposed epochs has allowed a conclusion to be made on the higher probability of the low-water period in the actual inflow to Lake Baikal that takes place on the increasing branch of solar activity of its 11-year cycle. The higher probability of a high-water period is observed on the decreasing branch of solar activity from the 2nd to the 5th year after its maximum. Probabilistic method

  4. Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

    Science.gov (United States)

    Wu, Hua'an; Zeng, Bo; Zhou, Meng

    2017-11-15

    High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

  5. Analysis and discussion of the most recent forecasts on energy demand for major industrial nations with a view to the avoidance of greenhouse gases

    International Nuclear Information System (INIS)

    Jochem, E.; Herz, H.; Mannsbart, W.

    1993-09-01

    In the future, when individual governments in their negotiations within the framework convention on climate change refer to their national energy demand assessments, any resulting energy forecasts are likely to be criticized in the scientific public because to date most of these energy demand assessments vary greatly in their essential basic assumptions (e.g., the future economic development, the oil price, the development of transportation, and so forth). For this reason the last ''official'' energy demand estimates for each country have been evaluated by a network of competent energy economy institutes in the countries having the highest emissions of climate-relevant gases (USA, Japan, the former Soviet Union, the EC). The work of this network had the following aim: to compare these most recent demand estimates and analyse their differences, to match each other's understanding of the most important basic assumptions and forecasting methods, and to agree on a reference skeleton for demand estimates, by means of which the assessments of different countries could be compared with each other. (orig.) [de

  6. Study the Effect of Value-Added of Services Sector on Forecasting of Electricity Demand in Services Sector due to Price Reform

    Directory of Open Access Journals (Sweden)

    Sayed Mahdi Mostafavi

    2016-07-01

    Full Text Available Electrical energy is as one of the important effective factors on economic growth and development. In recent decades, numerous studies in different countries to estimate and forecast electricity demand in different parts of the economy have been made. In this paper, using the method ARDL, estimation and forecasting of electricity demand in the services sector of Iran are determined for the time period from 1983 to 2012. Estimated equations show that the added value of the services sector and a significant positive impact on the demand for electricity in this sector. The price elasticity for services sector is smaller than 1 due to low electricity prices and subsidized electricity. Hence, electricity prices have little impact on the demand for electricity. The results of the estimate represents a long-term relationship between the variables in the services sector. In this paper, based on amendments to the law on subsidies and estimated values, anticipated electricity demand until the end of the fifth development plan was carried out. The results indicate an increase in power consumption in the services sector.

  7. Japan's actual energy supply/demand in 1986 and background - drastically changing economic/energy situations upset plans and forecasts by a wide margin

    Energy Technology Data Exchange (ETDEWEB)

    Fujime, K

    1987-05-01

    In 1986 the value of the yen soared and there was a lowering of interest rates and a slump in crude oil prices. These drastic changes in economic/energy situations brought about a completely different picture of Japan's energy supply and demand from originally expected. Energy demand from large industrial users was lowered and impacts of price fluctuations on energy supply and demand were uneven. Topics covered in the paper are: economic/industrial trends; energy price trends; actual energy supply and demand including electricity, oil, town gas, coal and LNG (liquefied natural gas); trends of major energy-consuming industries and energy consumption including steel industry, paper/pulp industry, cement industry and petrochemical industry; plans/forecasts completely off the track due to drastically changing economic/energy situations.

  8. Radiology Resident Supply and Demand: A Regional Perspective.

    Science.gov (United States)

    Pfeifer, Cory M

    2017-09-01

    Radiology was subject to crippling deficits in the number of jobs available to graduates of training programs from 2012 through 2015. As the specialty transitions to the assimilation of osteopathic training programs and the welcoming of direct competition from new integrated interventional radiology programs, the assessment of growth in radiology training positions over the 10 years preceding this pivotal time will serve to characterize the genesis of the crisis while inspiring stakeholders to avoid similar negative fluctuations in the future. The number of per capita radiology trainees in each region was derived from data published by the National Resident Matching Program, as were annual match statistics over the years 2012 through 2016. Data regarding new interventional radiology and diagnostic radiology enrollees were also obtained from the National Resident Matching Program. The seven states with the most per capita radiology residents were in the Mid-Atlantic and Northeastern United States in both 2006 and 2016, and three of these seven also showed the greatest per capita growth over the course of the 10 years studied. New radiology programs were accredited during the peak of the job shortage. Integrated interventional radiology training created 24 de novo radiology residents in the 2017 match. Fill rates are weakly positively correlated with program size. Unregulated radiology program growth persisted during the decade leading up to 2016. The region with the fewest jobs available since 2012 is also home to the greatest number of per capita radiology residents. Numerous published opinions during the crisis did not result in enforced policy change. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  9. Modeling Ontario regional electricity system demand using a mixed fixed and random coefficients approach

    Energy Technology Data Exchange (ETDEWEB)

    Hsiao, C.; Mountain, D.C.; Chan, M.W.L.; Tsui, K.Y. (University of Southern California, Los Angeles (USA) McMaster Univ., Hamilton, ON (Canada) Chinese Univ. of Hong Kong, Shatin)

    1989-12-01

    In examining the municipal peak and kilowatt-hour demand for electricity in Ontario, the issue of homogeneity across geographic regions is explored. A common model across municipalities and geographic regions cannot be supported by the data. Considered are various procedures which deal with this heterogeneity and yet reduce the multicollinearity problems associated with regional specific demand formulations. The recommended model controls for regional differences assuming that the coefficients of regional-seasonal specific factors are fixed and different while the coefficients of economic and weather variables are random draws from a common population for any one municipality by combining the information on all municipalities through a Bayes procedure. 8 tabs., 41 refs.

  10. The wind forecasting improvement project. Description and results from the Southern study region

    Energy Technology Data Exchange (ETDEWEB)

    Freedman, Jeffrey [AWS Truepower LLC, Albany, NY (United States); Benjamin, Stan; Wilczak, James [National Oceanic and Atmospheric Administration, Washington, DC and Boulder, CO (United States)] [and others

    2012-07-01

    The Wind Forecasting Improvement Project (WFIP) is a multi-year U.S. Department of Energy (DOE)/National Oceanographic and Atmospheric Administration (NOAA) sponsored study whose main purpose is to demonstrate the scientific and economic benefits of additional atmospheric observations and model enhancements on wind energy production forecasts. WFIP covers two geographical regions of the U.S.: (1) the upper Great Plains, or Northern Study Area, and (2) most of Texas-the Southern Study Area. The Southern campaign is being led by AWS Truepower LLC, and includes a team of private, government, and academic partners with collective experience and expertise in all facets required to ensure a successful completion of the project. In addition presenting a summary of the state-of-the-art forecasting techniques used and phenomena-based analysis mentioned above, a brief synopsis of how ''lessons learned'' from the WFIP Southern Study Area can be articulated and applied to other wind resource regions will be described. (orig.)

  11. Tropical deforestation: balancing regional development demands and global environmental concerns

    Energy Technology Data Exchange (ETDEWEB)

    Wood, A B [US Dept. of State, Washington, DC (USA)

    1990-01-01

    Over half of the world's tropical closed forests, which contain the greatest biodiversity, are found in just three countries: Brazil, Indonesia, and Zaire. Accelerated conversion of tropical forests is occurring because of several interlocking socio-economic and political factors: inequitable land distribution, entrenched rural poverty, and rapidly growing populations which push landless and near-landless peasants on to forest lands that are often infertile. If rates instead of absolute numbers are used to measure the severity of deforestation, Nigeria, Argentina, India, Thailand, Myanmar (Burma), Ecquador, and above all Ivory Coast stand out as countries facing an immediate deforestation crisis. Local management of forest resources, however, can be very contentious and complicated, with overlapping government agencies, competing economic interests, and ambiguous regulations. Without capital investment and entrepreneurial initiatives, residents of forest regions may have no alternative but to farm increasingly infertile soils. Non-governmental organizations, such as the World Wildlife Fund are playing leading roles in innovative debt-for-nature swaps and other forest conservation efforts. International development agencies, such as the World Bank, may play the leading role in conservation and reforestation efforts through their financial assistance programmes. The media, as a global information network, has become a powerful influence on the debate over deforestation. The Third World, bearing an increasingly heavy burden of payments to lending institutions that in 1988 surpassed 50 billion US dollars, will make a strong case that it cannot afford widespread forest conservation.

  12. 75 FR 14103 - Version One Regional Reliability Standard for Resource and Demand Balancing

    Science.gov (United States)

    2010-03-24

    ... current regional Reliability Standard was developed and used under a manual interchange transaction...] Version One Regional Reliability Standard for Resource and Demand Balancing March 18, 2010. AGENCY... section 215 of the Federal Power Act, the Commission proposes to remand a revised regional Reliability...

  13. Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain

    OpenAIRE

    Qingyou Yan; Chao Qin; Mingjian Nie; Le Yang

    2018-01-01

    Due to the deregulation of retail electricity market, consumers can choose retail electric suppliers freely, and market entities are facing fierce competition because of the increasing number of new entrants. Under these circumstances, forecasting the changes in all market entities, when market share stabilized, is important for suppliers making marketing decisions. In this paper, a market share forecasting model was established based on Markov chain, and a system dynamics model was construct...

  14. Assessment of Industrial Load for Demand Response across U.S. Regions of the Western Interconnect

    Energy Technology Data Exchange (ETDEWEB)

    Starke, Michael [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Alkadi, Nasr [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ma, Ookie [USDOE Office of Energy Efficiency and Renewable Energy (EERE), Washington, DC (United States)

    2013-09-01

    Demand response has the ability to both increase power grid reliability and potentially reduce operating system costs. Understanding the role of demand response in grid modeling has been difficult due to complex nature of the load characteristics compared to the modeled generation and the variation in load types. This is particularly true of industrial loads, where hundreds of different industries exist with varying availability for demand response. We present a framework considering industrial loads for the development of availability profiles for demand response that can provide more regional understanding and can be inserted into analysis software for further study.

  15. The Role of Demand Resources In Regional Transmission Expansion Planning and Reliable Operations

    Energy Technology Data Exchange (ETDEWEB)

    Kirby, Brendan J [ORNL

    2006-07-01

    Investigating the role of demand resources in regional transmission planning has provided mixed results. On one hand there are only a few projects where demand response has been used as an explicit alternative to transmission enhancement. On the other hand there is a fair amount of demand response in the form of energy efficiency, peak reduction, emergency load shedding, and (recently) demand providing ancillary services. All of this demand response reduces the need for transmission enhancements. Demand response capability is typically (but not always) factored into transmission planning as a reduction in the load which must be served. In that sense demand response is utilized as an alternative to transmission expansion. Much more demand response is used (involuntarily) as load shedding under extreme conditions to prevent cascading blackouts. The amount of additional transmission and generation that would be required to provide the current level of reliability if load shedding were not available is difficult to imagine and would be impractical to build. In a very real sense demand response solutions are equitably treated in every region - when proposed, demand response projects are evaluated against existing reliability and economic criteria. The regional councils, RTOs, and ISOs identify needs. Others propose transmission, generation, or responsive load based solutions. Few demand response projects get included in transmission enhancement plans because few are proposed. But this is only part of the story. Several factors are responsible for the current very low use of demand response as a transmission enhancement alternative. First, while the generation, transmission, and load business sectors each deal with essentially the same amount of electric power, generation and transmission companies are explicitly in the electric power business but electricity is not the primary business focus of most loads. This changes the institutional focus of each sector. Second

  16. Regional air-quality forecasting for the Pacific Northwest using MOPITT/TERRA assimilated carbon monoxide MOZART-4 forecasts as a near real-time boundary condition

    Directory of Open Access Journals (Sweden)

    F. L. Herron-Thorpe

    2012-06-01

    Full Text Available Results from a regional air quality forecast model, AIRPACT-3, were compared to AIRS carbon monoxide column densities for the spring of 2010 over the Pacific Northwest. AIRPACT-3 column densities showed high correlation (R > 0.9 but were significantly biased (~25% with consistent under-predictions for spring months when there is significant transport from Asia. The AIRPACT-3 CO bias relative to AIRS was eliminated by incorporating dynamic boundary conditions derived from NCAR's MOZART forecasts with assimilated MOPITT carbon monoxide. Changes in ozone-related boundary conditions derived from MOZART forecasts are also discussed and found to affect background levels by ± 10 ppb but not found to significantly affect peak ozone surface concentrations.

  17. The integration of Price Responsive Demand into Regional Transmission Organization (RTO) wholesale power markets and system operations

    International Nuclear Information System (INIS)

    Centolella, Paul

    2010-01-01

    A number of states and utilities are pursuing demand response based on dynamic and time-differentiated retail prices and utility investments in Advanced Metering Infrastructure (AMI), often as part of Smart Grid initiatives. These developments could produce large amounts of Price Responsive Demand, demand that predictably responds to changes in wholesale prices. Price Responsive Demand could provide significant reliability and economic benefits. However, existing RTO tariffs present potential barriers to the development of Price Responsive Demand. Effectively integrating Price Responsive Demand into RTO markets and operations will require changes in demand forecasting, scarcity pricing reform, synchronization of scarcity pricing with capacity markets, tracking voluntary hedging by price responsive loads, and a non-discriminatory approach in curtailments in capacity emergencies. The article describes changes in RTO policies and systems needed incorporate Price Responsive Demand. (author)

  18. Sizewell: UK power demand

    International Nuclear Information System (INIS)

    Anon.

    1986-01-01

    The Sizewell Inquiry was about whether the next power stations to be built in the UK should be nuclear or coal and, if nuclear, PWRs or AGRs. During the period of the Inquiry forecasts of demand for electricity were low. Now, however, it seems that the forecast demand is much increased. This uncertainty in demand and the wide regional variations are examined in some detail. Facts and figures on electricity sales (area by area) are presented. Also the minutes of supply lost per consumer per year. These show that security of supply is also a problem. It is also shown that the way electricity is used has changed. Whilst electricity generation has been changing to large-scale, centralised power stations the demand patterns may make smaller scale, quickly-constructed units more sensible. The questions considered at the Sizewell Inquiry may, indeed, no longer be the right ones. (UK)

  19. EIA projections of coal supply and demand

    International Nuclear Information System (INIS)

    Klein, D.E.

    1989-01-01

    Contents of this report include: EIA projections of coal supply and demand which covers forecasted coal supply and transportation, forecasted coal demand by consuming sector, and forecasted coal demand by the electric utility sector; and policy discussion

  20. The energy supply and demand outlook in the Asia-Pacific region

    International Nuclear Information System (INIS)

    Fesharaki, F.

    1993-01-01

    The 1980s witnessed spectacular growth rates in the Asia-Pacific region. While the relationship between economic growth and energy consumption is not necessarily one-to-one, energy is a required input for economic activity and trade. Energy demand growth in the Asia-Pacific region has been accordingly rapid. At this point in history, oil and economic growth are so inter-related that changes in one invariably have major repercussions on the other. During the coming decade, continued economic growth is foreseen for the Asia-Pacific region, coupled with the fastest rate of oil demand growth of any region on earth. Pressure will come to bear on the regional oil and gas markets, since demand growth will take place concurrently with a decline in the availability of local, low-sulfur crudes. The region will become even more dependent on imports of Middle Eastern crude, which will result in a higher-sulfur crude slate. Moreover, we anticipate that the existing and planned refinery complexes will lack the capacity and the flexibility to fully satisfy product demand. The consequence will be a higher level of refined product imports. The paper looks in greater detail at the supply and demand situation with respect to oil and natural gas, at regional oil import dependency and refining capacity. (10 figures). (author)

  1. Two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty

    International Nuclear Information System (INIS)

    Huang, Yun-Hsun; Wu, Jung-Hua; Hsu, Yu-Ju

    2016-01-01

    Traditional electricity supply planning models regard the electricity demand as a deterministic parameter and require the total power output to satisfy the aggregate electricity demand. But in today's world, the electric system planners are facing tremendously complex environments full of uncertainties, where electricity demand is a key source of uncertainty. In addition, electricity demand patterns are considerably different for different regions. This paper developed a multi-region optimization model based on two-stage stochastic programming framework to incorporate the demand uncertainty. Furthermore, the decision tree method and Monte Carlo simulation approach are integrated into the model to simplify electricity demands in the form of nodes and determine the values and probabilities. The proposed model was successfully applied to a real case study (i.e. Taiwan's electricity sector) to show its applicability. Detail simulation results were presented and compared with those generated by a deterministic model. Finally, the long-term electricity development roadmap at a regional level could be provided on the basis of our simulation results. - Highlights: • A multi-region, two-stage stochastic programming model has been developed. • The decision tree and Monte Carlo simulation are integrated into the framework. • Taiwan's electricity sector is used to illustrate the applicability of the model. • The results under deterministic and stochastic cases are shown for comparison. • Optimal portfolios of regional generation technologies can be identified.

  2. Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

    International Nuclear Information System (INIS)

    Ekman, Annica

    2000-02-01

    Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO 2 (a)), sulfate in air (SO 4 (a)), sulfate in cloud water (SO 4 (aq)) and hydrogen peroxide (H 2 O 2 ). Modeled concentrations of SO 2 (a), SO 4 (a) and SO 4 (aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO 2 (a) concentration and underpredicts the SO 4 (a) concentration. The agreement between modeled and observed SO 4 (aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO 2 (a) and 2-2.5 days for SO 4 (a). For SO 2 (a) this is in accordance with earlier simulated global turnover times, but for SO 4 (a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO 4 (a) and SO 4 (aq), if an additional oxidant for converting SO 2 (a) to SO 4 (a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations

  3. Electricity demand in Kazakhstan

    International Nuclear Information System (INIS)

    Atakhanova, Zauresh; Howie, Peter

    2007-01-01

    Properties of electricity demand in transition economies have not been sufficiently well researched mostly due to data limitations. However, information on the properties of electricity demand is necessary for policy makers to evaluate effects of price changes on different consumers and obtain demand forecasts for capacity planning. This study estimates Kazakhstan's aggregate demand for electricity as well as electricity demand in the industrial, service, and residential sectors using regional data. Firstly, our results show that price elasticity of demand in all sectors is low. This fact suggests that there is considerable room for price increases necessary to finance generation and distribution system upgrading. Secondly, we find that income elasticity of demand in the aggregate and all sectoral models is less than unity. Of the three sectors, electricity demand in the residential sector has the lowest income elasticity. This result indicates that policy initiatives to secure affordability of electricity consumption to lower income residential consumers may be required. Finally, our forecast shows that electricity demand may grow at either 3% or 5% per year depending on rates of economic growth and government policy regarding price increases and promotion of efficiency. We find that planned supply increases would be sufficient to cover growing demand only if real electricity prices start to increase toward long-run cost-recovery levels and policy measures are implemented to maintain the current high growth of electricity efficiency

  4. Forecasting freight flows

    DEFF Research Database (Denmark)

    Lyk-Jensen, Stéphanie

    2011-01-01

    Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...... constitute a valuable input to freight models for forecasting future capacity problems.......Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...

  5. Long-term stability analysis and effect evaluation of energy supply and demand in asia and pacific regions

    Energy Technology Data Exchange (ETDEWEB)

    Choi, B.R. [Korea Energy Economics Institute, Euiwang (Korea, Republic of)

    1998-06-01

    The competition in the supply of energy in Northeast Asian countries including Korea is forecasted to be intensively unfolded because of the soaring energy consumption the region. It is because the consumption of coal in Asia will be curtailed due to heightened global attention to environment, the import of Middle Eastern crude oil will be largely increasing since crude oil production in China and Indonesia is experiencing a slow-down. Therefore, close international cooperation is requested for the smooth supply of crude oil and natural gas to back up the economic growth. This study checked the energy supply and demand status in the Asian region that is changing since the 1990s, and analyzed the pressure factors in the energy supply sector. It needs to develop the Far Eastern region of Russia intensively as the energy supply base together with the Middle Eastern oil- producing regions as the plan to alleviate the energy competition within the region and seek economic development concurrently. Since this region requires large- scale investment as an never-expedited area, multilateral cooperation including neighboring countries that need energy and international enterprises who are interested in the profitability of the business are urgently required. Therefore, it is necessary to form and operate Northeast Asia Energy Forum (NAEF) which is a centripetal point that will actually carry out the resources development of Northeast Asia. Korea who is at the centripetal point of Northeast Asian tension should actively participate and support in the formation of this organization within the region in order to utilize it as the cornerstone of national north and south unification. 41 refs., 7 figs., 53 tabs.

  6. Travel demand modeling for the small and medium sized MPOs in Illinois.

    Science.gov (United States)

    2011-09-01

    Travel demand modeling is an important tool in the transportation planning community. It helps forecast travel : characteristics into the future at various planning levels such as state, region and corridor. Using travel demand : modeling to evaluate...

  7. Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Pablo García

    2013-06-01

    Full Text Available Thanks to the built in intelligence (deployment of new intelligent devices and sensors in places where historically they were not present, the Smart Grid and Microgrid paradigms are able to take advantage from aggregated load forecasting, which opens the door for the implementation of new algorithms to seize this information for optimization and advanced planning. Therefore, accuracy in load forecasts will potentially have a big impact on key operation factors for the future Smart Grid/Microgrid-based energy network like user satisfaction and resource saving, and new methods to achieve an efficient prediction in future energy landscapes (very different from the centralized, big area networks studied so far. This paper proposes different improved models to forecast next day’s aggregated load using artificial neural networks, taking into account the variables that are most relevant for the aggregated. In particular, seven models based on the multilayer perceptron will be proposed, progressively adding input variables after analyzing the influence of climate factors on aggregated load. The results section presents the forecast from the proposed models, obtained from real data.

  8. Forecasting of Energy and Petroleum Consumption by Motor Transport in the Regions of the Russian Federation

    Directory of Open Access Journals (Sweden)

    Leontiy Viktorovich Eder

    2017-09-01

    Full Text Available The paper offers the directions for the improvement of methodological approach to forecasting the energy consumption in transport, taking into account special features of Russian regions. The authors developed a multivariate model allowing to predict the motor vehicle rate specified for the regions of the Russian Federation depending on the economic, social and institutional features. We formalized the dynamic (trend model for predicting the effectiveness of energy consumption per unit of the vehicle in Russia with details on Federal districts. In the study, in predicting the number of motor transport, the authors applied the methods of economic and mathematical simulation modelling based on the results of the econometric analysis for the calculation of the population having motor transport. In determining the potential specific energy consumption, we have aggregated trending patterns and convergence. The study has shown that by 2040, the number of passenger cars in Russia will grow to 57.1 million, and the total number of all types of road transport will grow by 14.9 million units to 66.2 million. The highest growth rates are predicted in the Central regions of Russia and in some areas of Siberia. The smallest growth rates are expected in the Chukotka Autonomous District, Kamchatka and Primorsky regions. Energy efficiency in transport and active introduction of alternative motor fuels, primarily methane, will reduce the consumption of gasoline and diesel fuel by motor transport. Thus, in the forecast period of 2018–2040, the consumption of petroleum products by motor transport will be reduced by 8.9 million tons: from 61,9 million tons of oil to 51.7 million tons of oil. The results of the study can be applied for the formulation of proposals on the creation of scientific and methodological apparatus to predict the development of transport sector and oil products supply in of the regions of Russia.

  9. Operational forecasting of daily temperatures in the Valencia Region. Part II: minimum temperatures in winter.

    Science.gov (United States)

    Gómez, I.; Estrela, M.

    2009-09-01

    Extreme temperature events have a great impact on human society. Knowledge of minimum temperatures during winter is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, winter minimum temperatures are considered a parameter of interest and concern since persistent cold-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict cold-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that low temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily minimum temperatures during winter over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the winter forecast period from 1 December 2007 - 31 March 2008. The results obtained are encouraging and indicate a good agreement between the observed and simulated minimum temperatures. Moreover, the model captures quite well the temperatures in the extreme cold episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia

  10. Operational forecasting of daily temperatures in the Valencia Region. Part I: maximum temperatures in summer.

    Science.gov (United States)

    Gómez, I.; Estrela, M.

    2009-09-01

    Extreme temperature events have a great impact on human society. Knowledge of summer maximum temperatures is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, summer maximum daily temperatures are considered a parameter of interest and concern since persistent heat-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict heat-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily maximum temperatures during summer over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the summer forecast period of 1 June - 30 September, 2007. The results obtained are encouraging and indicate a good agreement between the observed and simulated maximum temperatures. Moreover, the model captures quite well the temperatures in the extreme heat episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia, Spain).

  11. The regional geological hazard forecast based on rainfall and WebGIS in Hubei, China

    Science.gov (United States)

    Zheng, Guizhou; Chao, Yi; Xu, Hongwen

    2008-10-01

    Various disasters have been a serious threat to human and are increasing over time. The reduction and prevention of hazard is the largest problem faced by local governments. The study of disasters has drawn more and more attention mainly due to increasing awareness of the socio-economic impact of disasters. Hubei province, one of the highest economic developing provinces in China, suffered big economic losses from geo-hazards in recent years due to frequent geo-hazard events with the estimated damage of approximately 3000 million RMB. It is therefore important to establish an efficient way to mitigate potential damage and reduce losses of property and life derived from disasters. This paper presents the procedure of setting up a regional geological hazard forecast and information releasing system of Hubei province with the combination of advanced techniques such as World Wide Web (WWW), database online and ASP based on WEBGIS platform (MAPGIS-IMS) and rainfall information. A Web-based interface was developed using a three-tiered architecture based on client-server technology in this system. The study focused on the upload of the rainfall data, the definition of rainfall threshold values, the creation of geological disaster warning map and the forecast of geohazard relating to the rainfall. Its purposes are to contribute to the management of mass individual and regional geological disaster spatial data, help to forecast the conditional probabilities of occurrence of various disasters that might be posed by the rainfall, and release forecasting information of Hubei province timely via the internet throughout all levels of government, the private and nonprofit sectors, and the academic community. This system has worked efficiently and stably in the internet environment which is strongly connected with meteorological observatory. Environment Station of Hubei Province are making increased use of our Web-tool to assist in the decision-making process to analyze geo

  12. Forecasting Precipitation over the MENA Region: A Data Mining and Remote Sensing Based Approach

    Science.gov (United States)

    Elkadiri, R.; Sultan, M.; Elbayoumi, T.; Chouinard, K.

    2015-12-01

    We developed and applied an integrated approach to construct predictive tools with lead times of 1 to 12 months to forecast precipitation amounts over the Middle East and North Africa (MENA) region. The following steps were conducted: (1) acquire and analyze temporal remote sensing-based precipitation datasets (i.e. Tropical Rainfall Measuring Mission [TRMM]) over five main water source regions in the MENA area (i.e. Atlas Mountains in Morocco, Southern Sudan, Red Sea Hills of Yemen, and Blue Nile and White Nile source areas) throughout the investigation period (1998 to 2015), (2) acquire and extract monthly values for all of the climatic indices that are likely to influence the climatic patterns over the MENA region (e.g., Northern Atlantic Oscillation [NOI], Southern Oscillation Index [SOI], and Tropical North Atlantic Index [TNA]); and (3) apply data mining methods to extract relationships between the observed precipitation and the controlling factors (climatic indices) and use predictive tools to forecast monthly precipitation over each of the identified pilot study areas. Preliminary results indicate that by using the period from January 1998 until August 2012 for model training and the period from September 2012 to January 2015 for testing, precipitation can be successfully predicted with a three-months lead over South West Yemen, Atlas Mountains in Morocco, Southern Sudan, Blue Nile sources and White Nile sources with confidence (Pearson correlation coefficient: 0.911, 0.823, 0.807, 0.801 and 0.895 respectively). Future work will focus on applying this technique for prediction of precipitation over each of the climatically contiguous areas of the MENA region. If our efforts are successful, our findings will lead the way to the development and implementation of sound water management scenarios for the MENA countries.

  13. Forecasting of future earthquakes in the northeast region of India considering energy released concept

    Science.gov (United States)

    Zarola, Amit; Sil, Arjun

    2018-04-01

    This study presents the forecasting of time and magnitude size of the next earthquake in the northeast India, using four probability distribution models (Gamma, Lognormal, Weibull and Log-logistic) considering updated earthquake catalog of magnitude Mw ≥ 6.0 that occurred from year 1737-2015 in the study area. On the basis of past seismicity of the region, two types of conditional probabilities have been estimated using their best fit model and respective model parameters. The first conditional probability is the probability of seismic energy (e × 1020 ergs), which is expected to release in the future earthquake, exceeding a certain level of seismic energy (E × 1020 ergs). And the second conditional probability is the probability of seismic energy (a × 1020 ergs/year), which is expected to release per year, exceeding a certain level of seismic energy per year (A × 1020 ergs/year). The logarithm likelihood functions (ln L) were also estimated for all four probability distribution models. A higher value of ln L suggests a better model and a lower value shows a worse model. The time of the future earthquake is forecasted by dividing the total seismic energy expected to release in the future earthquake with the total seismic energy expected to release per year. The epicentre of recently occurred 4 January 2016 Manipur earthquake (M 6.7), 13 April 2016 Myanmar earthquake (M 6.9) and the 24 August 2016 Myanmar earthquake (M 6.8) are located in zone Z.12, zone Z.16 and zone Z.15, respectively and that are the identified seismic source zones in the study area which show that the proposed techniques and models yield good forecasting accuracy.

  14. A Method for the Monthly Electricity Demand Forecasting in Colombia based on Wavelet Analysis and a Nonlinear Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Cristhian Moreno-Chaparro

    2011-12-01

    Full Text Available This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA with Discrete Wavelet Transform (DWT; a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing. A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in prediction.

  15. Analysis and evaluation of forecasting methods and tools to predict future demand for secondary chemical-biological configuration items

    OpenAIRE

    Ritchey, Chris D.

    2013-01-01

    Approved for public release; distribution is unlimited As the Engineering Support Activity (ESA) for numerous consumable Chemical Biological items managed by the Defense Logistics Agency (DLA), Edgewood Chemical Biological Center (ECBC) must be able to complete reviews of all procurement packages within 15 calendar days. With such little lead time, it would be very beneficial if ECBC had the ability to forecast when DLA procurement actions will occur. This thesis presents an evaluation of ...

  16. Optimal wind-hydro solution for the Marmara region of Turkey to meet electricity demand

    International Nuclear Information System (INIS)

    Dursun, Bahtiyar; Alboyaci, Bora; Gokcol, Cihan

    2011-01-01

    Wind power technology is now a reliable electricity production system. It presents an economically attractive solution for the continuously increasing energy demand of the Marmara region located in Turkey. However, the stochastic behavior of wind speed in the Marmara region can lead to significant disharmony between wind energy production and electricity demand. Therefore, to overcome wind's variable nature, a more reliable solution would be to integrate hydropower with wind energy. In this study, a methodology to estimate an optimal wind-hydro solution is developed and it is subsequently applied to six typical different site cases in the Marmara region in order to define the most beneficial configuration of the wind-hydro system. All numerical calculations are based on the long-term wind speed measurements, electrical load demand and operational characteristics of the system components. -- Research highlights: → This study is the first application of a wind-hydro pumped storage system in Turkey. → The methodology developed in this study is applied to the six sites in the Marmara region of Turkey. A wind - hydro pumped storage system is proposed to meet the electric energy demand of the Marmara region.

  17. Natural gas supply and demand projections for the Asia-Pacific region

    International Nuclear Information System (INIS)

    Khin, J.A.

    1992-01-01

    The phenomenon of rapid economic growth in the Asia Pacific has inevitably led the countries of this region to expand and diversify their energy sources in order to satisfy their burgeoning energy demands. Natural gas has become an increasingly marketable energy source in this region benefitting from vast reserves and its advantages as an environmentally clean fuel. As a result of the impact of the two oil shocks of the 1970's on the Asia Pacific economy, the governments in the region set about the development of energy strategies which would make their national economies more resilient to the instabilities of world energy price and supply. The Japanese Gas Industry has estimated that the overall rise in demand for energy in Asia, set at an average rate of 3.7% per annum, will see a corresponding growth in demand for natural gas at 5%. Experts from a number of major oil companies, such as Exxon, expect an annual growth in the Asian natural gas market of 6.0 to 6.3%. These figures are over shadowed by the worldwide demand for natural gas which is expected to gain an 8% increase within the next two decades. Approximately 8.75% of the world's proven natural gas reserves are held in Asia-Pacific region (Table I). Most of the region's natural gas production will provide over 100 years of supply. A review of natural gas supply/demand in the Asia-Pacific region is presented in sub-regions, namely ASIAN, Northeast Asia, the Indian Subcontinent and Oceania

  18. Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Ekman, Annica [Stockholm Univ. (Sweden). Dept. of Meteorology

    2000-02-01

    Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO{sub 2}(a)), sulfate in air (SO{sub 4}(a)), sulfate in cloud water (SO{sub 4}(aq)) and hydrogen peroxide (H{sub 2}O{sub 2}). Modeled concentrations of SO{sub 2}(a), SO{sub 4}(a) and SO{sub 4}(aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO{sub 2}(a) concentration and underpredicts the SO{sub 4}(a) concentration. The agreement between modeled and observed SO{sub 4}(aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO{sub 2}(a) and 2-2.5 days for SO{sub 4}(a). For SO{sub 2}(a) this is in accordance with earlier simulated global turnover times, but for SO{sub 4}(a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO{sub 4}(a) and SO{sub 4}(aq), if an additional oxidant for converting SO{sub 2}(a) to SO{sub 4}(a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations.

  19. Methodological Aspects in Forecasting Innovation Development of Dairy Cattle Breeding in the Region

    Directory of Open Access Journals (Sweden)

    Natal’ya Aleksandrovna Medvedeva

    2016-07-01

    Full Text Available Due to the fact that Russia is now a member of the World Trade Organization, long-term forecasting becomes an objectively necessary condition that helps choose an effective science-based long-term strategy for development of dairy cattle breeding that would take into consideration intellectual and innovative characteristics. Current structure of available statistical information does not meet modern challenges of innovation development and does not reflect adequately the trends of ongoing changes. The paper suggests a system of indicators to analyze the status, development and prospects of dairy cattle breeding in the region; this system provides timely identification of emerging risks and threats of deviation from the specified parameters. The system included indicators contained in the current statistical reporting and new indicators of innovation development of the industry, the quality of human capital and the level of government support. When designing the system of indicators, we used several methodological aspects of the Oslo Manual, which the Federal State Statistics Service considers to be an official methodological document concerning the collection of information about innovation activities. A structured system of indicators shifts the emphasis in the analysis of the final results to the conditions and prerequisites that help achieve forecast performance indicators in the functioning of Russia’s economy under WTO rules and make substantiated management decisions

  20. Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

    Science.gov (United States)

    Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

    2013-02-01

    Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

  1. Demand Uncertainty

    DEFF Research Database (Denmark)

    Nguyen, Daniel Xuyen

    This paper presents a model of trade that explains why firms wait to export and why many exporters fail. Firms face uncertain demands that are only realized after the firm enters the destination. The model retools the timing of uncertainty resolution found in productivity heterogeneity models....... This retooling addresses several shortcomings. First, the imperfect correlation of demands reconciles the sales variation observed in and across destinations. Second, since demands for the firm's output are correlated across destinations, a firm can use previously realized demands to forecast unknown demands...... in untested destinations. The option to forecast demands causes firms to delay exporting in order to gather more information about foreign demand. Third, since uncertainty is resolved after entry, many firms enter a destination and then exit after learning that they cannot profit. This prediction reconciles...

  2. The supply and demand outlook for energy in the Asia-Pacific region

    International Nuclear Information System (INIS)

    Fridley, D.

    1993-01-01

    It is reported that years of strong economic growth in the Asia-Pacific region have resulted in unprecedented increases in energy demand in the region, particularly for oil and gas. The supply of oil and gas to the region will become more problematic as the decade progresses. Already 50% dependent on imported oil, this figure will rise to nearly 65% by 2000. Because high rates of domestic oil demand growth among traditional petroleum exporters -Indonesia, China and Malaysia - will absorb exportable surpluses, the region will find itself dependent on the Middle East for at least 90% of its imported oil needs by 2000. Currently linked to oil, LNG prices cannot justify the investments needed to bring new greenfield projects on line. With demand expected to exceed 67 million tonnes (Mt) in 2000 and 100 Mt in 2010, over 50 Mt of new capacity will be needed; satisfying this demand will necessitate a new pricing structure for LNG, raising the price substantially above the relative price of crude oil. 1 ref., 5 figs, 2 tabs

  3. A Study on Grid-Square Statistics Based Estimation of Regional Electricity Demand and Regional Potential Capacity of Distributed Generators

    Science.gov (United States)

    Kato, Takeyoshi; Sugimoto, Hiroyuki; Suzuoki, Yasuo

    We established a procedure for estimating regional electricity demand and regional potential capacity of distributed generators (DGs) by using a grid square statistics data set. A photovoltaic power system (PV system) for residential use and a co-generation system (CGS) for both residential and commercial use were taken into account. As an example, the result regarding Aichi prefecture was presented in this paper. The statistical data of the number of households by family-type and the number of employees by business category for about 4000 grid-square with 1km × 1km area was used to estimate the floor space or the electricity demand distribution. The rooftop area available for installing PV systems was also estimated with the grid-square statistics data set. Considering the relation between a capacity of existing CGS and a scale-index of building where CGS is installed, the potential capacity of CGS was estimated for three business categories, i.e. hotel, hospital, store. In some regions, the potential capacity of PV systems was estimated to be about 10,000kW/km2, which corresponds to the density of the existing area with intensive installation of PV systems. Finally, we discussed the ratio of regional potential capacity of DGs to regional maximum electricity demand for deducing the appropriate capacity of DGs in the model of future electricity distribution system.

  4. Regional electric power demand elasticities of Japan's industrial and commercial sectors

    International Nuclear Information System (INIS)

    Hosoe, Nobuhiro; Akiyama, Shu-ichi

    2009-01-01

    In the assessment and review of regulatory reforms in the electric power market, price elasticity is one of the most important parameters that characterize the market. However, price elasticity has seldom been estimated in Japan; instead, it has been assumed to be as small as 0.1 or 0 without proper examination of the empirical validity of such a priori assumptions. We estimated the regional power demand functions for nine regions, in order to quantify the elasticity, and found the short-run price elasticity to be 0.09-0.30 and the long-run price elasticity to be 0.12-0.56. Inter-regional comparison of our estimation results suggests that price elasticity in rural regions is larger than that in urban regions. Popular assumptions of small elasticity of 0.1, for example, could be suitable for examining Japan's aggregate power demand but not power demand functions that focus on respective regions. Furthermore, assumptions about smaller elasticity values such as 0.01 and 0 could not be supported statistically by this study.

  5. Regional electric power demand elasticities of Japan's industrial and commercial sectors

    Energy Technology Data Exchange (ETDEWEB)

    Hosoe, Nobuhiro [National Graduate Institute for Policy Studies, 7-22-1 Roppongi, Minato, Tokyo 106-8677 (Japan); Akiyama, Shu-ichi [Kushiro Public University of Economics, 4-1-1 Ashino, Kushiro, Hokkaido 085-8585 (Japan)

    2009-11-15

    In the assessment and review of regulatory reforms in the electric power market, price elasticity is one of the most important parameters that characterize the market. However, price elasticity has seldom been estimated in Japan; instead, it has been assumed to be as small as 0.1 or 0 without proper examination of the empirical validity of such a priori assumptions. We estimated the regional power demand functions for nine regions, in order to quantify the elasticity, and found the short-run price elasticity to be 0.09-0.30 and the long-run price elasticity to be 0.12-0.56. Inter-regional comparison of our estimation results suggests that price elasticity in rural regions is larger than that in urban regions. Popular assumptions of small elasticity of 0.1, for example, could be suitable for examining Japan's aggregate power demand but not power demand functions that focus on respective regions. Furthermore, assumptions about smaller elasticity values such as 0.01 and 0 could not be supported statistically by this study. (author)

  6. LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

    Directory of Open Access Journals (Sweden)

    HUSSEIN A. ABDULQADER

    2012-08-01

    Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

  7. The energy supply and demand outlook in the Asia-Pacific region

    International Nuclear Information System (INIS)

    Fesharaki, Fereidun; Yamaguchi, Nancy

    1992-01-01

    The 1980s witnessed spectacular growth rates in the Asia-Pacific region, the rising economic power of Japan, an unprecedented opening of China's economy, the emergence of the ''Four Tigers'' or the ''Little Dragons'' (Singapore, South Korea, Taiwan and Hong Kong), a rapid growth of exports from the region - in short, remarkable economic success and an increase in entrepreneurialism and the free-market philosophy. Even the United States, which historically has been Atlantic-oriented, sat up and took notice. While the relationship between economic growth and energy consumption is not necessarily one-to-one, energy is a required input for economic activity and trade. Energy demand growth in the Asia-Pacific region has been accordingly rapid. At this point in history, oil and economic growth are so interrelated that changes in one invariably have major repercussions on the other. During the coming decade, continued economic growth is foreseen for the Asia-Pacific region, coupled with the fastest rate of oil demand growth of any region on Earth. Pressure will come to bear on the regional oil and gas markets, since demand growth will take place concurrently with a decline in the availability of local, low-sulphur crudes. The region will become even more dependent on imports of Middle Eastern crude, which will result in a higher-sulphur crude slate. Moreover, we anticipate that the existing and planned refinery complexes will lack the capacity and the flexibility to fully satisfy product demand. The consequence will be a higher level of refined product imports. (author)

  8. The energy supply and demand outlook in the Asia-Pacific region

    Energy Technology Data Exchange (ETDEWEB)

    Fesharaki, Fereidun; Yamaguchi, Nancy (East-West Centre, Honolulu, Hawaii (US))

    The 1980s witnessed spectacular growth rates in the Asia-Pacific region, the rising economic power of Japan, an unprecedented opening of China's economy, the emergence of the ''Four Tigers'' or the ''Little Dragons'' (Singapore, South Korea, Taiwan and Hong Kong), a rapid growth of exports from the region - in short, remarkable economic success and an increase in entrepreneurialism and the free-market philosophy. Even the United States, which historically has been Atlantic-oriented, sat up and took notice. While the relationship between economic growth and energy consumption is not necessarily one-to-one, energy is a required input for economic activity and trade. Energy demand growth in the Asia-Pacific region has been accordingly rapid. At this point in history, oil and economic growth are so interrelated that changes in one invariably have major repercussions on the other. During the coming decade, continued economic growth is foreseen for the Asia-Pacific region, coupled with the fastest rate of oil demand growth of any region on Earth. Pressure will come to bear on the regional oil and gas markets, since demand growth will take place concurrently with a decline in the availability of local, low-sulphur crudes. The region will become even more dependent on imports of Middle Eastern crude, which will result in a higher-sulphur crude slate. Moreover, we anticipate that the existing and planned refinery complexes will lack the capacity and the flexibility to fully satisfy product demand. The consequence will be a higher level of refined product imports. (author).

  9. World oil demand's shift toward faster growing and less price-responsive products and regions

    International Nuclear Information System (INIS)

    Dargay, Joyce M.; Gately, Dermot

    2010-01-01

    Using data for 1971-2008, we estimate the effects of changes in price and income on world oil demand, disaggregated by product - transport oil, fuel oil (residual and heating oil), and other oil - for six groups of countries. Most of the demand reductions since 1973-74 were due to fuel-switching away from fuel oil, especially in the OECD; in addition, the collapse of the Former Soviet Union (FSU) reduced their oil consumption substantially. Demand for transport and other oil was much less price-responsive, and has grown almost as rapidly as income, especially outside the OECD and FSU. World oil demand has shifted toward products and regions that are faster growing and less price-responsive. In contrast to projections to 2030 of declining per-capita demand for the world as a whole - by the U.S. Department of Energy (DOE), International Energy Agency (IEA) and OPEC - we project modest growth. Our projections for total world demand in 2030 are at least 20% higher than projections by those three institutions, using similar assumptions about income growth and oil prices, because we project rest-of-world growth that is consistent with historical patterns, in contrast to the dramatic slowdowns which they project. (author)

  10. The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

    Science.gov (United States)

    Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

    2015-10-01

    The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.

  11. Toward Sub-seasonal to Seasonal Arctic Sea Ice Forecasting Using the Regional Arctic System Model (RASM)

    Science.gov (United States)

    Kamal, S.; Maslowski, W.; Roberts, A.; Osinski, R.; Cassano, J. J.; Seefeldt, M. W.

    2017-12-01

    The Regional Arctic system model has been developed and used to advance the current state of Arctic modeling and increase the skill of sea ice forecast. RASM is a fully coupled, limited-area model that includes the atmosphere, ocean, sea ice, land hydrology and runoff routing components and the flux coupler to exchange information among them. Boundary conditions are derived from NCEP Climate Forecasting System Reanalyses (CFSR) or Era Iterim (ERA-I) for hindcast simulations or from NCEP Coupled Forecast System Model version 2 (CFSv2) for seasonal forecasts. We have used RASM to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook (SIO) of the Sea Ice Prediction Network (SIPN). Each year, we produced three SIOs for the September minimum, initialized on June 1, July 1 and August 1. In 2016, predictions used a simple linear regression model to correct for systematic biases and included the mean September sea ice extent, the daily minimum and the week of the minimum. In 2017, we produced a 12-member ensemble on June 1 and July 1, and 28-member ensemble August 1. The predictions of September 2017 included the pan-Arctic and regional Alaskan sea ice extent, daily and monthly mean pan-Arctic maps of sea ice probability, concentration and thickness. No bias correction was applied to the 2017 forecasts. Finally, we will also discuss future plans for RASM forecasts, which include increased resolution for model components, ecosystem predictions with marine biogeochemistry extensions (mBGC) to the ocean and sea ice components, and feasibility of optional boundary conditions using the Navy Global Environmental Model (NAVGEM).

  12. Seasonal forecasting of lightning and thunderstorm activity in tropical and temperate regions of the world.

    Science.gov (United States)

    Dowdy, Andrew J

    2016-02-11

    Thunderstorms are convective systems characterised by the occurrence of lightning. Lightning and thunderstorm activity has been increasingly studied in recent years in relation to the El Niño/Southern Oscillation (ENSO) and various other large-scale modes of atmospheric and oceanic variability. Large-scale modes of variability can sometimes be predictable several months in advance, suggesting potential for seasonal forecasting of lightning and thunderstorm activity in various regions throughout the world. To investigate this possibility, seasonal lightning activity in the world's tropical and temperate regions is examined here in relation to numerous different large-scale modes of variability. Of the seven modes of variability examined, ENSO has the strongest relationship with lightning activity during each individual season, with relatively little relationship for the other modes of variability. A measure of ENSO variability (the NINO3.4 index) is significantly correlated to local lightning activity at 53% of locations for one or more seasons throughout the year. Variations in atmospheric parameters commonly associated with thunderstorm activity are found to provide a plausible physical explanation for the variations in lightning activity associated with ENSO. It is demonstrated that there is potential for accurately predicting lightning and thunderstorm activity several months in advance in various regions throughout the world.

  13. Sizing inventory of blood products in a blood bank at Brazil based on a model of inventory management and a demand forecast

    Directory of Open Access Journals (Sweden)

    Julia Lorena Marques Gurgel

    2014-02-01

    Full Text Available The management of the stocks of products derived from the blood processing collected in blood banks is a problem for health services in Brazil and the world. Quantify the stocks of these products in order to equalize the demand and supply is not a simple task. It's necessary ensure that the product is available when needed and in due time. However, there is no how overestimate these stocks given that the product is perishable and it is not easy the availability of raw material (blood for processing. There are few studies in Brazil, however, that discuss this issue. This study will focus on one Brazilian Hemocentro, which has faced the challenge of measure the demand for haemotherapic's products and establish parameters to control their stocks. Thus, it was sought to adapt a recent study realized out of the country, about sizing of stocks of a inventory for blood banks, combined with a forecast model of demand for blood derivatives subclassified by blood type. This control aims to increase the availability of the transfusion service, as it intends to reduce shortages and wastage of the blood collected.

  14. Regional Differences in the Price-Elasticity of Demand for Energy

    Energy Technology Data Exchange (ETDEWEB)

    Bernstein, M. A.; Griffin, J.

    2006-02-01

    At the request of the National Renewable Energy Laboratory (NREL), the RAND Corporation examined the relationship between energy demand and energy prices with the focus on whether the relationships between demand and price differ if these are examined at different levels of data resolution. In this case, RAND compares national, regional, state, and electric utility levels of data resolution. This study is intended as a first step in helping NREL understand the impact that spatial disaggregation of data can have on estimating the impacts of their programs. This report should be useful to analysts in NREL and other national laboratories, as well as to policy nationals at the national level. It may help them understand the complex relationships between demand and price and how these might vary across different locations in the United States.

  15. End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts

    NARCIS (Netherlands)

    Erdinç, O.; Taşcikaraogυlu, A.; Paterakis, N.G.; Eren, Y.; Catalão, J.P.S.

    2017-01-01

    There is a remarkable potential for implementing demand response (DR) strategies for several purposes, such as peak load reduction, frequency regulation, etc., by using thermostatically controllable appliances. In this paper, an end-user comfort violation minimization oriented DR strategy for

  16. iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

    Science.gov (United States)

    Sumi, S. J.; Ferreira, C.

    2017-12-01

    Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system

  17. Forecasting Forest Type and Age Classes in the Appalachian-Cumberland Subregion of the Central Hardwood Region

    Science.gov (United States)

    David N. Wear; Robert Huggett

    2011-01-01

    This chapter describes how forest type and age distributions might be expected to change in the Appalachian-Cumberland portions of the Central Hardwood Region over the next 50 years. Forecasting forest conditions requires accounting for a number of biophysical and socioeconomic dynamics within an internally consistent modeling framework. We used the US Forest...

  18. A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    2015-12-01

    Full Text Available This article proposes a new model to address the design problem of a sustainable regional logistics network with uncertainty in future logistics demand. In the proposed model, the future logistics demand is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to logistics service capacity and environmental impacts is incorporated to consider the sustainability of logistics network design. The proposed model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of future logistics demand aims to minimize a risk-averse objective by determining the optimal location and size of logistics parks with CO2 emission taxes consideration. The second stage after the uncertain logistics demand has been determined is a scenario-based stochastic logistics service route choices equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, genetic algorithm, and Gauss–Seidel decomposition approach, is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that total social welfare of the logistics system depends very much on the level of uncertainty in future logistics demand, capital budget for logistics parks, and confidence levels of the chance constraints.

  19. Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

    Directory of Open Access Journals (Sweden)

    Ramona Magno

    2018-02-01

    Full Text Available A drought-monitoring and forecasting system developed for the Tuscany region was improved in order to provide a semi-automatic, more detailed, timely and comprehensive operational service for decision making, water authorities, researchers and general stakeholders. Ground-based and satellite data from different sources (regional meteorological stations network, MODIS Terra satellite and CHIRPS/CRU precipitation datasets are integrated through an open-source, interoperable SDI (spatial data infrastructure based on PostgreSQL/PostGIS to produce vegetation and precipitation indices that allow following of the occurrence and evolution of a drought event. The SDI allows the dissemination of comprehensive, up-to-date and customizable information suitable for different end-users through different channels, from a web page and monthly bulletins, to interoperable web services, and a comprehensive climate service. The web services allow geospatial elaborations on the fly, and the geo-database can be increased with new input/output data to respond to specific requests or to increase the spatial resolution.

  20. Mapping Distribution and Forecasting Invasion of Prosopis juliflora in Ethiopia's Afar Region

    Science.gov (United States)

    West, A. M.; Wakie, T.; Luizza, M.; Evangelista, P.

    2014-12-01

    Invasion of non-native species is among the most critical threats to natural ecosystems and economies world-wide. Mesquite (which includes some 45 species) is an invasive deciduous tree which is known to have an array of negative impacts on ecosystems and rural livelihoods in arid and semi-arid regions around the world, dominating millions of hectares of land in Asia, Africa, Australia and the Americas. In Ethiopia, Prosopis juliflora (the only reported mesquite) is the most pervasive plant invader, threatening local livelihoods and the country's unique biodiversity. Due to its rapid spread and persistence, P. juliflora has been ranked as one of the leading threats to traditional land use, exceeded only by drought and conflict. This project utilized NASA's Earth Observing System (EOS) data and species distribution modeling to map current infestations of P. juliflora in the Afar region of northeastern Ethiopia, and forecast its suitable habitat across the entire country. This project provided a time and cost-effective strategy for conducting risk assessments of invasive mesquite and subsequent monitoring and mitigation efforts by land managers and local communities.

  1. Demand response evaluation and forecasting — Methods and results from the EcoGrid EU experiment

    DEFF Research Database (Denmark)

    Larsen, Emil Mahler; Pinson, Pierre; Leimgruber, Fabian

    2017-01-01

    Understanding electricity consumers participating in new demand response schemes is important for investment decisions and the design and operation of electricity markets. Important metrics include peak response, time to peak response, energy delivered, ramping, and how the response changes...... with respect to external conditions. Such characteristics dictate the services DR is capable of offering, like primary frequency reserves, peak load shaving, and system balancing. In this paper, we develop methods to characterise price-responsive demand from the EcoGrid EU demonstration in a way that was bid...... into a real-time market. EcoGrid EU is a smart grid experiment with 1900 residential customers who are equipped with smart meters and automated devices reacting to five-minute electricity pricing. Customers are grouped and analysed according to the manufacturer that controlled devices. A number of advanced...

  2. Integrated management of water resources demand and supply in irrigated agriculture from plot to regional scale

    Directory of Open Access Journals (Sweden)

    N. Schütze

    2016-05-01

    Full Text Available Growing water scarcity in agriculture is an increasing problem in future in many regions of the world. Recent trends of weather extremes in Saxony, Germany also enhance drought risks for agricultural production. In addition, signals of longer and more intense drought conditions during the vegetation period can be found in future regional climate scenarios for Saxony. However, those climate predictions are associated with high uncertainty and therefore, e.g. stochastic methods are required to analyze the impact of changing climate patterns on future crop water requirements and water availability. For assessing irrigation as a measure to increase agricultural water security a generalized stochastic approach for a spatial distributed estimation of future irrigation water demand is proposed, which ensures safe yields and a high water productivity at the same time. The developed concept of stochastic crop water production functions (SCWPF can serve as a central decision support tool for both, (i a cost benefit analysis of farm irrigation modernization on a local scale and (ii a regional water demand management using a multi-scale approach for modeling and implementation. The new approach is applied using the example of a case study in Saxony, which is dealing with the sustainable management of future irrigation water demands and its implementation.

  3. Water use demand in the Crans-Montana-Sierre region (Switzerland)

    Science.gov (United States)

    Bonriposi, M.; Reynard, E.

    2012-04-01

    Crans-Montana-Sierre is an Alpine touristic region located in the driest area of Switzerland (Rhone River Valley, Canton of Valais), with both winter (ski) and summer (e.g. golf) tourist activities. Climate change as well as societal and economic development will in future significantly modify the supply and consumption of water and, consequently, may fuel conflicts of interest. Within the framework of the MontanAqua project (www.montanaqua.ch), we are researching more sustainable water management options based on the co-ordination and adaptation of water demand to water availability under changing biophysical and socioeconomic conditions. This work intends to quantify current water uses in the area and consider future scenarios (around 2050). We have focused upon the temporal and spatial characteristics of resource demand, in order to estimate the spatial footprint of water use (drinking water, hydropower production, irrigation and artificial snowmaking), in terms of system, infrastructure, and organisation of supply. We have then quantified these as precisely as possible (at the monthly temporal scale and at the municipality spatial scale). When the quantity of water was not measurable for practical reasons or for lack of data, as for the case for irrigation or snowmaking, an alternative approach was applied. Instead of quantifying how much water was used, the stress was put on the water needs for irrigating agricultural land or on the optimal meteorological conditions necessary to produce artificial snow. A huge summer peak and a smaller winter peak characterize the current regional water consumption estimation. The summer peak is mainly caused by irrigation and secondly by drinking water demand. The winter peak is essentially due to drinking water and snowmaking. Other consumption peaks exist at the municipality scale but they cannot be observed at the regional scale. The results show a major variation in water demand between the 11 concerned municipalities and

  4. Impacts of Climate Change on Energy Consumption and Peak Demand in Buildings: A Detailed Regional Approach

    Energy Technology Data Exchange (ETDEWEB)

    Dirks, James A.; Gorrissen, Willy J.; Hathaway, John E.; Skorski, Daniel C.; Scott, Michael J.; Pulsipher, Trenton C.; Huang, Maoyi; Liu, Ying; Rice, Jennie S.

    2015-01-01

    This paper presents the results of numerous commercial and residential building simulations, with the purpose of examining the impact of climate change on peak and annual building energy consumption over the portion of the Eastern Interconnection (EIC) located in the United States. The climate change scenario considered (IPCC A2 scenario as downscaled from the CASCaDE data set) has changes in mean climate characteristics as well as changes in the frequency and duration of intense weather events. This investigation examines building energy demand for three annual periods representative of climate trends in the CASCaDE data set at the beginning, middle, and end of the century--2004, 2052, and 2089. Simulations were performed using the Building ENergy Demand (BEND) model which is a detailed simulation platform built around EnergyPlus. BEND was developed in collaboration with the Platform for Regional Integrated Modeling and Analysis (PRIMA), a modeling framework designed to simulate the complex interactions among climate, energy, water, and land at decision-relevant spatial scales. Over 26,000 building configurations of different types, sizes, vintages, and, characteristics which represent the population of buildings within the EIC, are modeled across the 3 EIC time zones using the future climate from 100 locations within the target region, resulting in nearly 180,000 spatially relevant simulated demand profiles for each of the 3 years. In this study, the building stock characteristics are held constant based on the 2005 building stock in order to isolate and present results that highlight the impact of the climate signal on commercial and residential energy demand. Results of this analysis compare well with other analyses at their finest level of specificity. This approach, however, provides a heretofore unprecedented level of specificity across multiple spectrums including spatial, temporal, and building characteristics. This capability enables the ability to

  5. An Operational Short-Term Forecasting System for Regional Hydropower Management

    Science.gov (United States)

    Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

    2017-12-01

    The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

  6. Application of a regional hurricane wind risk forecasting model for wood-frame houses.

    Science.gov (United States)

    Jain, Vineet Kumar; Davidson, Rachel Ann

    2007-02-01

    Hurricane wind risk in a region changes over time due to changes in the number, type, locations, vulnerability, and value of buildings. A model was developed to quantitatively estimate changes over time in hurricane wind risk to wood-frame houses (defined in terms of potential for direct economic loss), and to estimate how different factors, such as building code changes and population growth, contribute to that change. The model, which is implemented in a simulation, produces a probability distribution of direct economic losses for each census tract in the study region at each time step in the specified time horizon. By changing parameter values and rerunning the analysis, the effects of different changes in the built environment on the hurricane risk trends can be estimated and the relative effectiveness of hypothetical mitigation strategies can be evaluated. Using a case study application for wood-frame houses in selected counties in North Carolina from 2000 to 2020, this article demonstrates how the hurricane wind risk forecasting model can be used: (1) to provide insight into the dynamics of regional hurricane wind risk-the total change in risk over time and the relative contribution of different factors to that change, and (2) to support mitigation planning. Insights from the case study include, for example, that the many factors contributing to hurricane wind risk for wood-frame houses interact in a way that is difficult to predict a priori, and that in the case study, the reduction in hurricane losses due to vulnerability changes (e.g., building code changes) is approximately equal to the increase in losses due to building inventory growth. The potential for the model to support risk communication is also discussed.

  7. Shipping emission forecasts and cost-benefit analysis of China ports and key regions' control.

    Science.gov (United States)

    Liu, Huan; Meng, Zhi-Hang; Shang, Yi; Lv, Zhao-Feng; Jin, Xin-Xin; Fu, Ming-Liang; He, Ke-Bin

    2018-05-01

    China established Domestic Emission Control Area (DECA) for sulphur since 2015 to constrain the increasing shipping emissions. However, future DECA policy-makings are not supported due to a lack of quantitive evaluations. To investigate the effects of current and possible Chinese DECAs policies, a model is presented for the forecast of shipping emissions and evaluation of potential costs and benefits of an DECA policy package set in 2020. It includes a port-level and regional-level projection accounting for shipping trade volume growth, share of ship types, and fuel consumption. The results show that without control measures, both SO 2 and particulate matter (PM) emissions are expected to increase by 15.3-61.2% in Jing-Jin-Ji, the Yangtze River Delta, and the Pearl River Delta from 2013 to 2020. However, most emissions can be reduced annually by the establishment of a DECA that depends on the size of the control area and the fuel sulphur content limit. Costs range from 0.667 to 1.561 billion dollars (control regional shipping emissions) based on current fuel price. A social cost method shows the regional control scenarios benefit-cost ratios vary from 4.3 to 5.1 with large uncertainty. Chemical transportation model combined with health model method is used to get the monetary health benefits and then compared with the results from social cost method. This study suggests that Chinese DECAs will reduce the projected emissions at a favorable benefit-cost ratio, and furthermore proposes policy combinations that provide high cost-effective benefits as a reference for future policy-making. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

  8. Demand-driven water withdrawals by Chinese industry: a multi-regional input-output analysis

    Science.gov (United States)

    Zhang, Bo; Chen, Z. M.; Zeng, L.; Qiao, H.; Chen, B.

    2016-03-01

    With ever increasing water demands and the continuous intensification of water scarcity arising from China's industrialization, the country is struggling to harmonize its industrial development and water supply. This paper presents a systems analysis of water withdrawals by Chinese industry and investigates demand-driven industrial water uses embodied in final demand and interregional trade based on a multi-regional input-output model. In 2007, the Electric Power, Steam, and Hot Water Production and Supply sector ranks first in direct industrial water withdrawal (DWW), and Construction has the largest embodied industrial water use (EWU). Investment, consumption, and exports contribute to 34.6%, 33.3%, and 30.6% of the national total EWU, respectively. Specifically, 58.0%, 51.1%, 48.6%, 43.3%, and 37.5% of the regional EWUs respectively in Guangdong, Shanghai, Zhejiang, Jiangsu, and Fujian are attributed to international exports. The total interregional import/export of embodied water is equivalent to about 40% of the national total DWW, of which 55.5% is associated with the DWWs of Electric Power, Steam, and Hot Water Production and Supply. Jiangsu is the biggest interregional exporter and deficit receiver of embodied water, in contrast to Guangdong as the biggest interregional importer and surplus receiver. Without implementing effective water-saving measures and adjusting industrial structures, the regional imbalance between water availability and water demand tends to intensify considering the water impact of domestic trade of industrial products. Steps taken to improve water use efficiency in production, and to enhance embodied water saving in consumption are both of great significance for supporting China's water policies.

  9. A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

    Science.gov (United States)

    Pan, Yujie; Xue, Ming; Zhu, Kefeng; Wang, Mingjun

    2018-05-01

    A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/˜13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

  10. Forecasting typhoid fever incidence in the Cordillera administrative region in the Philippines using seasonal ARIMA models

    Science.gov (United States)

    Cawiding, Olive R.; Natividad, Gina May R.; Bato, Crisostomo V.; Addawe, Rizavel C.

    2017-11-01

    The prevalence of typhoid fever in developing countries such as the Philippines calls for a need for accurate forecasting of the disease. This will be of great assistance in strategic disease prevention. This paper presents a development of useful models that predict the behavior of typhoid fever incidence based on the monthly incidence in the provinces of the Cordillera Administrative Region from 2010 to 2015 using univariate time series analysis. The data used was obtained from the Cordillera Office of the Department of Health (DOH-CAR). Seasonal autoregressive moving average (SARIMA) models were used to incorporate the seasonality of the data. A comparison of the results of the obtained models revealed that the SARIMA (1,1,7)(0,0,1)12 with a fixed coefficient at the seventh lag produces the smallest root mean square error (RMSE), mean absolute error (MAE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model suggested that for the year 2016, the number of cases would increase from the months of July to September and have a drop in December. This was then validated using the data collected from January 2016 to December 2016.

  11. Classification of rainfall events for weather forecasting purposes in andean region of Colombia

    Science.gov (United States)

    Suárez Hincapié, Joan Nathalie; Romo Melo, Liliana; Vélez Upegui, Jorge Julian; Chang, Philippe

    2016-04-01

    This work presents a comparative analysis of the results of applying different methodologies for the identification and classification of rainfall events of different duration in meteorological records of the Colombian Andean region. In this study the work area is the urban and rural area of Manizales that counts with a monitoring hydro-meteorological network. This network is composed of forty-five (45) strategically located stations, this network is composed of forty-five (45) strategically located stations where automatic weather stations record seven climate variables: air temperature, relative humidity, wind speed and direction, rainfall, solar radiation and barometric pressure. All this information is sent wirelessly every five (5) minutes to a data warehouse located at the Institute of Environmental Studies-IDEA. With obtaining the series of rainfall recorded by the hydrometeorological station Palogrande operated by the National University of Colombia in Manizales (http://froac.manizales.unal.edu.co/bodegaIdea/); it is with this information that we proceed to perform behavior analysis of other meteorological variables, monitored at surface level and that influence the occurrence of such rainfall events. To classify rainfall events different methodologies were used: The first according to Monjo (2009) where the index n of the heavy rainfall was calculated through which various types of precipitation are defined according to the intensity variability. A second methodology that permitted to produce a classification in terms of a parameter β introduced by Rice and Holmberg (1973) and adapted by Llasat and Puigcerver, (1985, 1997) and the last one where a rainfall classification is performed according to the value of its intensity following the issues raised by Linsley (1977) where the rains can be considered light, moderate and strong fall rates to 2.5 mm / h; from 2.5 to 7.6 mm / h and above this value respectively for the previous classifications. The main

  12. Robust Optimization on Regional WCO-for-Biodiesel Supply Chain under Supply and Demand Uncertainties

    Directory of Open Access Journals (Sweden)

    Yong Zhang

    2016-01-01

    Full Text Available This paper aims to design a robust waste cooking oil- (WCO- for-biodiesel supply chain under WCO supply and price as well as biodiesel demand and price uncertainties, so as to improve biorefineries’ ability to cope with the poor environment. A regional supply chain is firstly introduced based on the biggest WCO-for-biodiesel company in Changzhou, Jiangsu province, and it comprises three components: WCO supplier, biorefinery, and demand zone. And then a robust mixed integer linear model with multiple objectives (economic, environmental, and social objectives is proposed for both biorefinery location and transportation plans. After that, a heuristic algorithm based on genetic algorithm is proposed to solve this model. Finally, the 27 cities in Yangtze River delta are adopted to verify the proposed models and methods, and the sustainability and robustness of biodiesel supply are discussed.

  13. Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling

    International Nuclear Information System (INIS)

    Dyson, Brian; Chang, N.-B.

    2005-01-01

    Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach - system dynamics modeling - for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool - Stella[reg]. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues

  14. Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada's Regional Air Quality Deterministic Prediction System.

    Science.gov (United States)

    So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

    2018-05-01

    Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode

  15. Regional allocation of biomass to U.S. energy demands under a portfolio of policy scenarios.

    Science.gov (United States)

    Mullins, Kimberley A; Venkatesh, Aranya; Nagengast, Amy L; Kocoloski, Matt

    2014-01-01

    The potential for widespread use of domestically available energy resources, in conjunction with climate change concerns, suggest that biomass may be an essential component of U.S. energy systems in the near future. Cellulosic biomass in particular is anticipated to be used in increasing quantities because of policy efforts, such as federal renewable fuel standards and state renewable portfolio standards. Unfortunately, these independently designed biomass policies do not account for the fact that cellulosic biomass can equally be used for different, competing energy demands. An integrated assessment of multiple feedstocks, energy demands, and system costs is critical for making optimal decisions about a unified biomass energy strategy. This study develops a spatially explicit, best-use framework to optimally allocate cellulosic biomass feedstocks to energy demands in transportation, electricity, and residential heating sectors, while minimizing total system costs and tracking greenhouse gas emissions. Comparing biomass usage across three climate policy scenarios suggests that biomass used for space heating is a low cost emissions reduction option, while biomass for liquid fuel or for electricity becomes attractive only as emissions reduction targets or carbon prices increase. Regardless of the policy approach, study results make a strong case for national and regional coordination in policy design and compliance pathways.

  16. Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

    Science.gov (United States)

    Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

    2018-02-01

    A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.

  17. Modeling the wind-fields of accidental releases with an operational regional forecast model

    International Nuclear Information System (INIS)

    Albritton, J.R.; Lee, R.L.; Sugiyama, G.

    1995-01-01

    The Atmospheric Release Advisory Capability (ARAC) is an operational emergency preparedness and response organization supported primarily by the Departments of Energy and Defense. ARAC can provide real-time assessments of atmospheric releases of radioactive materials at any location in the world. ARAC uses robust three-dimensional atmospheric transport and dispersion models, extensive geophysical and dose-factor databases, meteorological data-acquisition systems, and an experienced staff. Although it was originally conceived and developed as an emergency response and assessment service for nuclear accidents, the ARAC system has been adapted to also simulate non-radiological hazardous releases. For example, in 1991 ARAC responded to three major events: the oil fires in Kuwait, the eruption of Mt. Pinatubo in the Philippines, and the herbicide spill into the upper Sacramento River in California. ARAC's operational simulation system, includes two three-dimensional finite-difference models: a diagnostic wind-field scheme, and a Lagrangian particle-in-cell transport and dispersion scheme. The meteorological component of ARAC's real-time response system employs models using real-time data from all available stations near the accident site to generate a wind-field for input to the transport and dispersion model. Here we report on simulation studies of past and potential release sites to show that even in the absence of local meteorological observational data, readily available gridded analysis and forecast data and a prognostic model, the Navy Operational Regional Atmospheric Prediction System, applied at an appropriate grid resolution can successfully simulate complex local flows

  18. Market researches on demand for dietary and prophylactic nutrition in the Saratov Region

    Directory of Open Access Journals (Sweden)

    Nikitinа T. A.

    2016-09-01

    Full Text Available In competitive and constantly changing market conditions special attention has been paid to market researches. The results of these studies become the basis for formation of sales estimates, and projected levels of revenues and profits from sales. Market researches are the essential condition to make offered goods successful at the market. It is market researches that help companies to realize the control of consumers' needs changing. The market researches on demand for dietary and prophylactic nutrition in the Saratov Region have been the research objective. In accordance with the given objective the following goals have been formed: 1 finding out the most spread diseases among the population; 2 determination of the average age of people suffering from one or another disease; 3 studying demand of products with food fiber additives; 4 analysis of respondent's preference regarding the addition of natural vegetable additives to foodstuff; 5 demand for dietary and prophylactic foodstuff among the population. Since the survey has been carried out among people suffering from various diseases, particular attention during the market research has been turned to the medical and prophylactic properties of food fibers to find out the attitude of the respondents to functional foodstuff. The dynamics of noninfectious diseases, the population health status depending on age, and increase of overweight people during 2012–2014 have been shown. As a result of questionnaire data processing it has been ascertained that the population is of low awareness regarding the necessity to consume foodstuff with radioprotective and chelator properties. The researches have demonstrated that dietary and prophylactic foodstuff is in demand and consequently the development of such products is reasonable.

  19. A methodology for extracting knowledge rules from artificial neural networks applied to forecast demand for electric power; Uma metodologia para extracao de regras de conhecimento a partir de redes neurais artificiais aplicadas para previsao de demanda por energia eletrica

    Energy Technology Data Exchange (ETDEWEB)

    Steinmetz, Tarcisio; Souza, Glauber; Ferreira, Sandro; Santos, Jose V. Canto dos; Valiati, Joao [Universidade do Vale do Rio dos Sinos (PIPCA/UNISINOS), Sao Leopoldo, RS (Brazil). Programa de Pos-Graduacao em Computacao Aplicada], Emails: trsteinmetz@unisinos.br, gsouza@unisinos.br, sferreira, jvcanto@unisinos.br, jfvaliati@unisinos.br

    2009-07-01

    We present a methodology for the extraction of rules from Artificial Neural Networks (ANN) trained to forecast the electric load demand. The rules have the ability to express the knowledge regarding the behavior of load demand acquired by the ANN during the training process. The rules are presented to the user in an easy to read format, such as IF premise THEN consequence. Where premise relates to the input data submitted to the ANN (mapped as fuzzy sets), and consequence appears as a linear equation describing the output to be presented by the ANN, should the premise part holds true. Experimentation demonstrates the method's capacity for acquiring and presenting high quality rules from neural networks trained to forecast electric load demand for several amounts of time in the future. (author)

  20. Forecasting the demand on solar water heating systems and their energy savings potential during the period 2001-2005 in Jordan

    International Nuclear Information System (INIS)

    Kablan, M.M.

    2003-01-01

    Jordan is an example of a developing country that depends almost exclusively on imported oil. Luckily, Jordan is blessed with good solar energy resources. However, only 24% of Jordanian families are installing solar water heating systems (SWHS). The objective of this research is to forecast the yearly demand on SWHS by the household sector during the period 2001-2005 and to compute the potential energy savings throughout the investigated period due to the use of SWHS. It is found that the net energy collected over the entire investigated period is about 1454.4 million kW h. In addition, the capital savings over the entire investigated period is estimated to be 46.28 million US$ if SWHS are used to heat water instead of the commonly used LPG gas cookers. The results of the research may assist decision makers in the energy sector to implement more comprehensive plans that encourage more families to install SWHS and save on imported oil

  1. Combined desalination, water reuse, and aquifer storage and recovery to meet water supply demands in the GCC/MENA region

    KAUST Repository

    Ghaffour, NorEddine; Missimer, Thomas M.; Amy, Gary L.

    2013-01-01

    it an attractive option for water supply even in countries where desalination was unthinkable in the past. In the GCC/MENA region, operating records show that water demand is relatively constant during the year, while power demand varies considerably with a high

  2. MAG4 Versus Alternative Techniques for Forecasting Active-Region Flare Productivity

    Science.gov (United States)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2014-01-01

    MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events. MAG4 does not forecast that a flare will occur at a particular time in the next 24 or 48 hours; rather the probability of one occurring.

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-01-01 to 2007-09-05

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) was developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center, the...

  4. Research on Demand for Bus Transport and Transport Habits of High School Students in Žilina Region

    Directory of Open Access Journals (Sweden)

    Konečný Vladimír

    2017-11-01

    Full Text Available The paper deals with the analysis of demand for bus transport to examine determinants of demand and practices of high school students based on survey of their transport habits in Žilina Region. Transport habits of students are individual and variable in time. This group of passengers is dependent on public passenger transport services because of their travelling to schools. Significant part of demand for public passenger transport is also formed by this this group of passengers. The knowledge of student's transport habits may help in process of adaptation of offering and quality of transport serviceability what may subsequently stabilize demand for public passenger transport.

  5. A review of operational, regional-scale, chemical weather forecasting models in Europe

    Czech Academy of Sciences Publication Activity Database

    Kukkonen, J.; Olsson, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.H.; PoupKou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.E.J.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, Kryštof

    2012-01-01

    Roč. 12, - (2012), s. 1-87 ISSN 1680-7316 Institutional research plan: CEZ:AV0Z10300504 Keywords : chemical weather * numerical models * operational forecasting * air Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 5.510, year: 2012

  6. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model

    Energy Technology Data Exchange (ETDEWEB)

    Cadenas, Erasmo [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro (Mexico); Rivera, Wilfrido [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)

    2010-12-15

    In this paper the wind speed forecasting in the Isla de Cedros in Baja California, in the Cerro de la Virgen in Zacatecas and in Holbox in Quintana Roo is presented. The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. In order to do wind speed forecasting Hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANN) models were developed. The ARIMA models were first used to do the wind speed forecasting of the time series and then with the obtained errors ANN were built taking into account the nonlinear tendencies that the ARIMA technique could not identify, reducing with this the final errors. Once the Hybrid models were developed 48 data out of sample for each one of the sites were used to do the wind speed forecasting and the results were compared with the ARIMA and the ANN models working separately. Statistical error measures such as the mean error (ME), the mean square error (MSE) and the mean absolute error (MAE) were calculated to compare the three methods. The results showed that the Hybrid models predict the wind velocities with a higher accuracy than the ARIMA and ANN models in the three examined sites. (author)

  7. A hierarchical Bayesian spatio-temporal model to forecast trapped particle fluxes over the SAA region

    Czech Academy of Sciences Publication Activity Database

    Suparta, W.; Gusrizal, G.; Kudela, Karel; Isa, Z.

    2017-01-01

    Roč. 28, č. 3 (2017), s. 357-370 ISSN 1017-0839 R&D Projects: GA MŠk EF15_003/0000481 Institutional support: RVO:61389005 Keywords : trapped particle * spatio-temporal * hierarchical Bayesian * forecasting Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 0.752, year: 2016

  8. Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support

    NARCIS (Netherlands)

    C.G.H. Diks (Cees); V. Panchenko (Valentyn); O. Sokolinskiy (Oleg); D.J.C. van Dijk (Dick)

    2013-01-01

    textabstractThis paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample)

  9. Comparing the accuracy of copula-based multivariate density forecasts in selected regions of support

    NARCIS (Netherlands)

    Diks, C.; Panchenko, V.; Sokolinskiy, O.; van Dijk, D.

    2013-01-01

    This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional

  10. Seasonal regional forecast of the minimum sea ice extent in the LapteV Sea

    Science.gov (United States)

    Tremblay, B.; Brunette, C.; Newton, R.

    2017-12-01

    Late winter anomaly of sea ice export from the peripheral seas of the Atctic Ocean was found to be a useful predictor for the minimum sea ice extent (SIE) in the Arctic Ocean (Williams et al., 2017). In the following, we present a proof of concept for a regional seasonal forecast of the min SIE for the Laptev Sea based on late winter coastal divergence quantified using a Lagrangian Ice Tracking System (LITS) forced with satellite derived sea-ice drifts from the Polar Pathfinder. Following Nikolaeva and Sesterikov (1970), we track an imaginary line just offshore of coastal polynyas in the Laptev Sea from December of the previous year to May 1 of the following year using LITS. Results show that coastal divergence in the Laptev Sea between February 1st and May 1st is best correlated (r = -0.61) with the following September minimum SIE in accord with previous results from Krumpen et al. (2013, for the Laptev Sea) and Williams et a. (2017, for the pan-Arctic). This gives a maximum seasonal predictability of Laptev Sea min SIE anomalies from observations of approximately 40%. Coastal ice divergence leads to formation of thinner ice that melts earlier in early summer, hence creating areas of open water that have a lower albedo and trigger an ice-albedo feedback. In the Laptev Sea, we find that anomalies of coastal divergence in late winter are amplified threefold to result in the September SIE. We also find a correlation coefficient r = 0.49 between February-March-April (FMA) anomalies of coastal divergence with the FMA averaged AO index. Interestingly, the correlation is stronger, r = 0.61, when comparing the FMA coastal divergence anomalies to the DJFMA averaged AO index. It is hypothesized that the AO index at the beginning of the winter (and the associated anomalous sea ice export) also contains information that impact the magnitude of coastal divergence opening later in the winter. Our approach differs from previous approaches (e.g. Krumpen et al and Williams et al

  11. Demand prediction model for regional railway services considering spatial effects between stations

    Energy Technology Data Exchange (ETDEWEB)

    Cordera Piñera, R.; Sañudo, R.; Olio, L. Dell' ; Ibeas, A.

    2016-07-01

    The railways are a priority transport mode for the European Union given their safety record and environmental sustainability. Therefore it is important to have quantitative models available which allow passenger demand for rail travel to be simulated for planning purposes and to evaluate different policies. The aim of this article is to specify and estimate trip distribution models between railway stations by considering the most influential demand variables. Two types of models were estimated: Poisson regression and gravity. The input data were the ticket sales on a regional line in Cantabria (Spain) which were provided by the Spanish railway infrastructure administrator (ADIF – RAM). The models have also considered the possible existence of spatial effects between train stations. The results show that the models have a good fit to the available data, especial the gravity models constrained by origins and destinations. Furthermore, the gravity models which considered the existence of spatial effects between stations had a significantly better fit than the Poisson models and the gravity models that did not consider this phenomenon. The proposed models have therefore been shown to be good support tools for decision making in the field of railway planning. (Author)

  12. Spatial electric load forecasting

    CERN Document Server

    Willis, H Lee

    2002-01-01

    Containing 12 new chapters, this second edition contains offers increased-coverage of weather correction and normalization of forecasts, anticipation of redevelopment, determining the validity of announced developments, and minimizing risk from over- or under-planning. It provides specific examples and detailed explanations of key points to consider for both standard and unusual utility forecasting situations, information on new algorithms and concepts in forecasting, a review of forecasting pitfalls and mistakes, case studies depicting challenging forecast environments, and load models illustrating various types of demand.

  13. ECONOMIC & DEMOGRAPHIC CHARACTERICTICS, SOCIAL CAPITAL AND DEMAND FOR LIFE INSURANCE: EVIDENCE FROM CENTRAL REGION OF SRI LANKA

    Directory of Open Access Journals (Sweden)

    Sisira Kumara NARADDA GAMAGE

    2016-08-01

    Full Text Available This paper presents findings of the determinants of demand for life insurance in the central region of Sri Lanka.  It is a novel study in the sense that it incorporated social capital as a determinant of demand for life insurance. Primary data has been collected through random sampling and the logistic model was used to examine the determinants of the demand for life insurance. Results confirmed that gender, income, trust and social capital has significant effect on demand for life insurance in the study area. Income and trust came out positive contributors of life insurance demand. However, it is worthy to note that although income has a positive effect on life insurance demand but its odds ratio makes it less important factor to influence demand for life insurance. Gender has deteriorated effect on demand for life insurance indicating that male household head less likely to purchase life insurance. Similarly, social capital also has a negative impact on demand for life insurance. Other determinants like age, religious status, working status, and education, has not significant effect on life insurance demand. Policies are recommended on research findings.

  14. A statistical data assimilation method for seasonal streamflow forecasting to optimize hydropower reservoir management in data-scarce regions

    Science.gov (United States)

    Arsenault, R.; Mai, J.; Latraverse, M.; Tolson, B.

    2017-12-01

    Probabilistic ensemble forecasts generated by the ensemble streamflow prediction (ESP) methodology are subject to biases due to errors in the hydrological model's initial states. In day-to-day operations, hydrologists must compensate for discrepancies between observed and simulated states such as streamflow. However, in data-scarce regions, little to no information is available to guide the streamflow assimilation process. The manual assimilation process can then lead to more uncertainty due to the numerous options available to the forecaster. Furthermore, the model's mass balance may be compromised and could affect future forecasts. In this study we propose a data-driven approach in which specific variables that may be adjusted during assimilation are defined. The underlying principle was to identify key variables that would be the most appropriate to modify during streamflow assimilation depending on the initial conditions such as the time period of the assimilation, the snow water equivalent of the snowpack and meteorological conditions. The variables to adjust were determined by performing an automatic variational data assimilation on individual (or combinations of) model state variables and meteorological forcing. The assimilation aimed to simultaneously optimize: (1) the error between the observed and simulated streamflow at the timepoint where the forecasts starts and (2) the bias between medium to long-term observed and simulated flows, which were simulated by running the model with the observed meteorological data on a hindcast period. The optimal variables were then classified according to the initial conditions at the time period where the forecast is initiated. The proposed method was evaluated by measuring the average electricity generation of a hydropower complex in Québec, Canada driven by this method. A test-bed which simulates the real-world assimilation, forecasting, water release optimization and decision-making of a hydropower cascade was

  15. Development of a methodology for monthly forecasting of surface fires of Colombia's vegetation cover, an application to north Andean region

    International Nuclear Information System (INIS)

    Gonzalez Hernandez, Yolanda; Rangel CH, Jesus Orlando

    2004-01-01

    In the present article a methodology is presented for the forecasting of the monthly risk of surface fires of the vegetation cover in Colombia, based on the analysis of meteorological components and variables of climatic and anthropic variability involved in fire risks of the north Andean region. The methodology enables one to regionalize the country, with fire prediction purposes in mind, into ten sub-regions, in each one of which seven height levels are defined to make up separate regions of study. For each of these, a database is built to feed both the logistic regression models and the Poisson models, which identify the variables independent from, and/or associated with the presence or absence of fires

  16. Traffic forecasts ignoring induced demand

    DEFF Research Database (Denmark)

    Næss, Petter; Nicolaisen, Morten Skou; Strand, Arvid

    2012-01-01

    the model calculations included only a part of the induced traffic, the difference in cost-benefit results compared to the model excluding all induced traffic was substantial. The results show lower travel time savings, more adverse environmental impacts and a considerably lower benefitcost ratio when...... induced traffic is partly accounted for than when it is ignored. By exaggerating the economic benefits of road capacity increase and underestimating its negative effects, omission of induced traffic can result in over-allocation of public money on road construction and correspondingly less focus on other...... performance of a proposed road project in Copenhagen with and without short-term induced traffic included in the transport model. The available transport model was not able to include long-term induced traffic resulting from changes in land use and in the level of service of public transport. Even though...

  17. Pent-up demand for surgery in the Manaus metropolitan region: A population-based cross-sectional study.

    Science.gov (United States)

    Ferreira, Priscila Gusmão da Silva; Galvao, Tais Freire; Silva, Marcus Tolentino

    2017-08-01

    Waiting lines in healthcare reflect an imbalance between the availability and the demand for medical services. This study aimed to analyze the prevalence and factors associated with the pent-up demand for surgical procedures in the Manaus metropolitan region.We performed a population-based cross-sectional study in 2015. Pent-up demand was based on self-report by the participants; those who reported waiting were contacted by phone to clarify the nature and reasons for the experienced delay.We interviewed 4001 adults in their households. The pent-up demand for surgical procedures in the Manaus metropolitan region was 14% (95% confidence interval: 13-15%). The surgical specialties with the highest demand included orthopedics (18.1%), gynecology (17.0%), ophthalmology (12.4%), neurosurgery (10.8%), and general surgery (10.2%). The main reason for not accessing services was their lack of availability in the public health system, leading some patients to pay for procedures in private facilities. The populations most affected by pent-up demand included elderly individuals, women, and housewives.Pent-up demand for surgical procedures is a common problem in the Manaus metropolitan region and thus requires coordinated actions to optimize access to and capacity of the healthcare system.

  18. A Coupled Snow Operations-Skier Demand Model for the Ontario (Canada) Ski Region

    Science.gov (United States)

    Pons, Marc; Scott, Daniel; Steiger, Robert; Rutty, Michelle; Johnson, Peter; Vilella, Marc

    2016-04-01

    The multi-billion dollar global ski industry is one of the tourism subsectors most directly impacted by climate variability and change. In the decades ahead, the scholarly literature consistently projects decreased reliability of natural snow cover, shortened and more variable ski seasons, as well as increased reliance on snowmaking with associated increases in operational costs. In order to develop the coupled snow, ski operations and demand model for the Ontario ski region (which represents approximately 18% of Canada's ski market), the research utilized multiple methods, including: a in situ survey of over 2400 skiers, daily operations data from ski resorts over the last 10 years, climate station data (1981-2013), climate change scenario ensemble (AR5 - RCP 8.5), an updated SkiSim model (building on Scott et al. 2003; Steiger 2010), and an agent-based model (building on Pons et al. 2014). Daily snow and ski operations for all ski areas in southern Ontario were modeled with the updated SkiSim model, which utilized current differential snowmaking capacity of individual resorts, as determined from daily ski area operations data. Snowmaking capacities and decision rules were informed by interviews with ski area managers and daily operations data. Model outputs were validated with local climate station and ski operations data. The coupled SkiSim-ABM model was run with historical weather data for seasons representative of an average winter for the 1981-2010 period, as well as an anomalously cold winter (2012-13) and the record warm winter in the region (2011-12). The impact on total skier visits and revenues, and the geographic and temporal distribution of skier visits were compared. The implications of further climate adaptation (i.e., improving the snowmaking capacity of all ski areas to the level of leading resorts in the region) were also explored. This research advances system modelling, especially improving the integration of snow and ski operations models with

  19. Regional forecast model for the Olea pollen season in Extremadura (SW Spain)

    Science.gov (United States)

    Fernández-Rodríguez, Santiago; Durán-Barroso, Pablo; Silva-Palacios, Inmaculada; Tormo-Molina, Rafael; Maya-Manzano, José María; Gonzalo-Garijo, Ángela

    2016-10-01

    The olive tree ( Olea europaea) is a predominantly Mediterranean anemophilous species. The pollen allergens from this tree are an important cause of allergic problems. Olea pollen may be relevant in relation to climate change, due to the fact that its flowering phenology is related to meteorological parameters. This study aims to investigate airborne Olea pollen data from a city on the SW Iberian Peninsula, to analyse the trends in these data and their relationships with meteorological parameters using time series analysis. Aerobiological sampling was conducted from 1994 to 2013 in Badajoz (SW Spain) using a 7-day Hirst-type volumetric sampler. The main Olea pollen season lasted an average of 34 days, from May 4th to June 7th. The model proposed to forecast airborne pollen concentrations, described by one equation. This expression is composed of two terms: the first term represents the resilience of the pollen concentration trend in the air according to the average concentration of the previous 10 days; the second term was obtained from considering the actual pollen concentration value, which is calculated based on the most representative meteorological variables multiplied by a fitting coefficient. Due to the allergenic characteristics of this pollen type, it should be necessary to forecast its short-term prevalence using a long record of data in a city with a Mediterranean climate. The model obtained provides a suitable level of confidence to forecast Olea airborne pollen concentration.

  20. Forecasting the Optimal Factors of Formation of the Population Savings as the Basis for Investment Resources of the Regional Economy

    Directory of Open Access Journals (Sweden)

    Odintsova Tetiana M.

    2017-04-01

    Full Text Available The article is aimed at studying the optimal factors of formation of the population savings as the basis for investment resources of the regional economy. A factorial (nonlinear correlative-regression analysis of the formation of savings of the population of Ukraine was completed. On its basis a forecast of the optimal structure and volumes of formation of the population incomes was carried out taking into consideration impact of fundamental factors on these incomes. Such approach provides to identify the marginal volumes of tax burden, population savings, and capital investments, directed to economic growth.

  1. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    This paper presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$58 billion. The study shows with very high statistical significance...... that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risk. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts...... forecasting. Highly inaccurate traffic forecasts combined with large standard deviations translate into large financial and economic risks. But such risks are typically ignored or downplayed by planners and decision-makers, to the detriment of social and economic welfare. The paper presents the data...

  2. Methodologies Used by Midwest Region States for Studying Teacher Supply and Demand. Issues & Answers. REL 2009-080

    Science.gov (United States)

    Lindsay, James J.; Wan, Yinmei; Gossin-Wilson, Will

    2009-01-01

    This report describes how state education agencies in the Midwest Region monitor teacher supply, demand, and shortage; details why they monitor these data; and offers estimates of the monetary costs incurred in performing such studies. This study responds to a request from state education agencies in the Midwest Region (Illinois, Indiana, Iowa,…

  3. Forecast of useful energy for the TIMES-Norway model

    International Nuclear Information System (INIS)

    Rosenberg, Eva

    2012-01-01

    A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)

  4. Forecast of useful energy for the TIMES-Norway model

    Energy Technology Data Exchange (ETDEWEB)

    Rosenberg, Eva

    2012-07-25

    A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)

  5. Recent developments in the applications of the Regional Atmospheric Modeling System (RAMS) for emergency response planning and operational forecasting at the Kennedy Space Center

    International Nuclear Information System (INIS)

    Lyons, W.A.; Tremback, C.J.

    1996-01-01

    The authors will summarize ten years of developing and applying the Regional Atmospheric Modeling System (RAMS) to emergency response and operational dispersion forecasting at the Kennedy Space Center (KSC). RAMS forms the core of two workstation-based operational systems, ERDAS (the Emergency Response Dose Assessment System) and PROWESS (Parallelized RAMS Operational Weather Simulation System) which are undergoing extensive operational testing prior to potential deployment as part of the range forecasting system at KSC. RAMS has been interfaced with HYPACT (the Hybrid Particle and Concentration Transport Model) to produce detailed 3-D dispersion forecasts from a variety of sources including cold spills, routine launch operations, and explosive conflagrations of launch vehicles

  6. Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply

    International Nuclear Information System (INIS)

    Hargreaves, Anthony; Cheng, Vicky; Deshmukh, Sandip; Leach, Matthew; Steemers, Koen

    2017-01-01

    Highlights: • An innovative model for testing combinations of spatial planning and decentralised energy supply. • An improved method of modelling the spatial variability of energy consumption per dwelling type. • Shows how spatial planning would affect the future carbon reduction of decentralised supply. • Forecasts the future carbon reduction and costs of retrofitting and decentralised supply. • A method of forecasting how residential space would affect the suitability of decentralised supply. - Abstract: Low carbon energy supply technologies are increasingly used at the building and community scale and are an important part of the government decarbonisation strategy. However, with their present state of development and costs, many of these decentralised technologies rely on public subsidies to be financially viable. It is questionable whether they are cost effective compared to other ways of reducing carbon emissions, such as decarbonisation of conventional supply and improving the energy efficiency of dwellings. Previous studies have found it difficult to reliably estimate the future potential of decentralised supply because this depends on the available residential space which varies greatly within a city region. To address this problem, we used an integrated modelling framework that converted the residential density forecasts of a regional model into a representation of the building dimensions and land of the future housing stock. This included a method of estimating the variability of the dwellings and residential land. We present the findings of a case study of the wider south east regions of England that forecasted the impacts of energy efficiency and decentralised supply scenarios to year 2031. Our novel and innovative method substantially improves the spatial estimates of energy consumption compared to building energy models that only use standard dwelling typologies. We tested the impact of an alternative spatial planning policy on the future

  7. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

    CSIR Research Space (South Africa)

    Das, Sonali

    2010-01-01

    Full Text Available and Gertler, 1995), but also because changes in house prices tend to have important wealth effects on consumption (International Monetary Fund, 2000) and investment (Topel and Rosen, 1988), the importance of forecasting house price infl ation is vital, since... sections, respectively, lay out the DFM and outline the basics of the VAR, the Minnesota-type BVARs, and the SBVARs based on the fi rst-order spatial contiguity (FOSC) and the random walk averaging (RWA) priors developed by LeSage and Pan (1995) and Le...

  8. Analysis of judicial demands in health at the Regional Health Department XII

    Directory of Open Access Journals (Sweden)

    Leticia Florido Povinske Domingues

    2017-08-01

    Full Text Available The management of lawsuits in health represents a challenge for most Brazilian municipalities. Many papers described in the literature characterized properly the problem as well as discussed the repercussions on the Unified Health System.In this context, the objective of this study was to analyze the situation of health judicial processes at the twelfth Regional Department in Registro, São Paulo´s state, evaluating the profile of the users who claim in court the couverage of treatment´s costs as well as examination, procedure or medication. For this, we analyzed data on lawsuits in the health field at the twelfth Regional Department in Registro (SP from january 2009 to october 2015.The variables studied were gender, age, municipality of origin of the lawsuit, the mentioned disease, the medical prescription origin, specialty of the prescriber, type of lawsuit triggered, year of the lawsuit, entity judicially triggered and requested items. It was analyzed thirty-eight lawsuits against the twelfth Regional Department, it was observed the prevalence of the female gender, age group above 51 years and originating from the municipality of Registro (SP.The most of the judicial actions are for care given at the Unified Health System, by prescribers of specialty in Clinical Medicine and diagnosed with Diabetes Mellitus. The processes were characterized in the majority by judicial actions called Ordinary Rite, against the State. On the analyzed cases, 92% requested only medications and of these 11% supplements like vitamins and enteral diets.The number of patients who have been served through legal actions in the last three years reached 47% of the total cases registered during the period of the seven years analyzed and the approximate cost was R$ 1,340,000.00.This study contributes to the diagnosis of the processes related to the health judicialization in the region studied. The results showed a predominance of processes which comes from of patients

  9. Improving weather forecasts for wind energy applications

    Science.gov (United States)

    Kay, Merlinde; MacGill, Iain

    2010-08-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  10. Improving weather forecasts for wind energy applications

    International Nuclear Information System (INIS)

    Kay, Merlinde; MacGill, Iain

    2010-01-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  11. Socioeconomic Forecasting : [Technical Summary

    Science.gov (United States)

    2012-01-01

    Because the traffic forecasts produced by the Indiana : Statewide Travel Demand Model (ISTDM) are driven by : the demographic and socioeconomic inputs to the model, : particular attention must be given to obtaining the most : accurate demographic and...

  12. Improving regional climate and hydrological forecasting following the record setting flooding across the Lake Ontario - St. Lawrence River system

    Science.gov (United States)

    Gronewold, A.; Seglenieks, F.; Bruxer, J.; Fortin, V.; Noel, J.

    2017-12-01

    In the spring of 2017, water levels across Lake Ontario and the upper St. Lawrence River exceeded record high levels, leading to widespread flooding, damage to property, and controversy over regional dam operating protocols. Only a few years earlier, water levels on Lakes Superior, Michigan, and Huron (upstream of Lake Ontario) had dropped to record low levels leading to speculation that either anthropogenic controls or climate change were leading to chronic water loss from the Great Lakes. The contrast between low water level conditions across Earth's largest lake system from the late 1990s through 2013, and the rapid rise prior to the flooding in early 2017, underscores the challenges of quantifying and forecasting hydrologic impacts of rising regional air and water temperatures (and associated changes in lake evaporation) and persistent increases in long-term precipitation. Here, we assess the hydrologic conditions leading to the recent record flooding across the Lake Ontario - St. Lawrence River system, with a particular emphasis on understanding the extent to which those conditions were consistent with observed and anticipated changes in historical and future climate, and the extent to which those conditions could have been anticipated through improvements in seasonal climate outlooks and hydrological forecasts.

  13. Socioeconomic and travel demand forecasts for Virginia and potential policy responses : a report for VTrans2035 : Virginia's statewide multimodal transportation plan.

    Science.gov (United States)

    2009-01-01

    VTrans2035, Virginia's statewide multimodal transportation plan, requires 25-year forecasts of socioeconomic and travel activity. Between 2010 and 2035, daily vehicle miles traveled (DVMT) will increase between 35% and 45%, accompanied by increases i...

  14. Towards a better knowledge of flash flood forecasting at the Three Gorges Region: Progress over the past decade and challenges ahead

    Science.gov (United States)

    Li, Zhe; Yang, Dawen; Yang, Hanbo; Wu, Tianjiao; Xu, Jijun; Gao, Bing; Xu, Tao

    2015-04-01

    The study area, the Three Gorges Region (TGR), plays a critical role in predicting the floods drained into the Three Gorges Reservoir, as reported local floods often exceed 10000m3/s during rainstorm events and trigger fast as well as significant impacts on the Three Gorges Reservoir's regulation. Meanwhile, it is one of typical mountainous areas in China, which is located in the transition zone between two monsoon systems: the East Asian monsoon and the South Asian (Indian) monsoon. This climatic feature, combined with local irregular terrains, has shaped complicated rainfall-runoff regimes in this focal region. However, due to the lack of high-resolution hydrometeorological data and physically-based hydrologic modeling framework, there was little knowledge about rainfall variability and flood pattern in this historically ungauged region, which posed great uncertainties to flash flood forecasting in the past. The present study summarize latest progresses of regional flash floods monitoring and prediction, including installation of a ground-based Hydrometeorological Observation Network (TGR-HMON), application of a regional geomorphology-based hydrological model (TGR-GBHM), development of an integrated forecasting and modeling system (TGR-INFORMS), and evaluation of quantitative precipitation estimations (QPE) and quantitative precipitation forecasting (QPF) products in TGR flash flood forecasting. With these continuing efforts to improve the forecasting performance of flash floods in TGR, we have addressed several critical issues: (1) Current observation network is still insufficient to capture localized rainstorms, and weather radar provides valuable information to forecast flash floods induced by localized rainstorms, although current radar QPE products can be improved substantially in future; (2) Long-term evaluation shows that the geomorphology-based distributed hydrologic model (GBHM) is able to simulate flash flooding processes reasonably, while model

  15. Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers

    Science.gov (United States)

    Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.

    2015-12-01

    The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast

  16. Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions

    Directory of Open Access Journals (Sweden)

    Stepchenko Arthur

    2015-12-01

    Full Text Available Time series of earth observation based estimates of vegetation inform about variations in vegetation at the scale of Latvia. A vegetation index is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation. NDVI index is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. In this paper, we make a one-step-ahead prediction of 7-daily time series of NDVI index using Markov chains. The choice of a Markov chain is due to the fact that a Markov chain is a sequence of random variables where each variable is located in some state. And a Markov chain contains probabilities of moving from one state to other.

  17. Evaluating the Impact of AIRS Observations on Regional Forecasts at the SPoRT Center

    Science.gov (United States)

    Zavodsky, Bradley

    2011-01-01

    NASA Short-term Prediction Research and Transition (SPoRT) Center collaborates with operational partners of different sizes and operational goals to improve forecasts using targeted projects and data sets. Modeling and DA activities focus on demonstrating utility of NASA data sets and capabilities within operational systems. SPoRT has successfully assimilated the Atmospheric Infrared Sounder (AIRS) radiance and profile data. A collaborative project is underway with the Joint Center for Satellite Data Assimilation (JCSDA) to use AIRS profiles to better understand the impact of AIRS radiances assimilated within Gridpoint Statistical Interpolation (GSI) in hopes of engaging the operational DA community in a reassessment of assimilation methodologies to more effectively assimilate hyperspectral radiances.

  18. Imbalance of demand and supply for regionalized injury services: a case study in Greece.

    Science.gov (United States)

    Petridou, E; Gatsoulis, N; Dessypris, N; Skalkidis, Y; Voros, D; Papadimitriou, Y; Trichopoulos, D

    2000-04-01

    To study regionalized acute injury services on an island with high seasonal fluctuation of the population at risk. Of 9432 individuals with traumatic injuries who contacted the hospital during 1996, 1204 were hospitalized. Information was recorded on several injury-related clinical and sociodemographic variables. Possible residual disabilities, 6 months after the injury, were also assessed. Injury Severity Score (ISS), clinical outcome and duration of hospitalization, odds of transfer to other institutions. Non-residents, whether Greek or foreign nationals are hospitalized for shorter periods. Motor vehicle accident victims are hospitalized on average for 15% longer. Injury victims admitted on a Friday are hospitalized for a longer period. Finally, ISS is a powerful positive predictor of duration of hospitalization. Male injury victims, those injured during late night or early morning and patients injured in July are more likely to be transferred to another institution. Age of the patient and ISS are powerful and independent predictors of an unfavourable outcome. The extra demand created by injured tourists is reflected in the seasonality of admissions for injuries. The district hospital of Kerkyra cannot be considered as deficient in comparison to other district hospitals. Nevertheless, the suboptimal function of the hospital, with respect to injuries, is reflected in the high proportion of injured patients transferred when the injury occurs outside the full working schedule of the hospital. Patients with burns, bone fractures or dislocations and head injuries or concussion are transferred with an overall frequency of about 15%--too high to be compatible with a well functioning secondary care institution.

  19. Seemingly Unrelated Regression Approach for GSTARIMA Model to Forecast Rain Fall Data in Malang Southern Region Districts

    Directory of Open Access Journals (Sweden)

    Siti Choirun Nisak

    2016-06-01

    Full Text Available Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS assumes the variance-covariance matrix has a constant error ~(, but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR approach is used to accommodate the weakness of the OLS. SUR assumption is ~(, for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS. Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1(1,12,36,(0,(1-SUR with determination coefficient generated with the average of 57.726%.

  20. Modeling the Impacts of Changes in Freight Demand, Infrastructure Improvements and Policy Measures on a Metropolitan Region

    Science.gov (United States)

    2018-01-01

    In this research, a policy framework was developed and used as a tool to determine the impacts of change in truck traffic on a regional level as a result of policy change. To achieve the objective, three demand models were used in the framework which...

  1. Projection of regional demand for labour force under the terms of industry modernization

    Directory of Open Access Journals (Sweden)

    Igor Aleksandrovich Bayev

    2011-06-01

    Full Text Available The article represents the results of research devoted to the problem of demand for labour force projection. The two main priorities of Russian economic development — modernization and innovation — are declared as the factors influencing labour market in a rather intrinsic and specific way. The research of dependence between GDP growth rate per occupied in the most developed countries is conducted and shows the positive influence the innovation process imposes on the demand for labour force. The particular problem is proved to be semi-structured. The selforganisation approach to this problem is proposed and helps to detect that the main shaping process of labour market dynamics is industry modernization. The trend of modernization influence on demand for labour force is revealed and taken under consideration while developing the mathematical model, enabling to predict the demand for labour force with not more than 2% mean absolute percentage error.

  2. Methodical Approaches To Analysis And Forecasting Of Development Fuel And Energy Complex And Gas Industry In The Region

    Directory of Open Access Journals (Sweden)

    Vladimir Andreyevich Tsybatov

    2014-12-01

    Full Text Available Fuel and energy complex (FEC is one of the main elements of the economy of any territory over which intertwine the interests of all economic entities. To ensure economic growth of the region should ensure that internal balance of energy resources, which should be developed with account of regional specifics of economic growth and energy security. The study examined the status of this equilibrium, indicating fuel and energy balance of the region (TEB. The aim of the research is the development of the fuel and energy balance, which will allow to determine exactly how many and what resources are not enough to ensure the regional development strategy and what resources need to be brought in. In the energy balances as the focus of displays all issues of regional development, so thermopile is necessary as a mechanism of analysis of current issues, economic development, and in the forward-looking version — as a tool future vision for the fuel and energy complex, energy threats and ways of overcoming them. The variety of relationships in the energy sector with other sectors and aspects of society lead to the fact that the development of the fuel and energy balance of the region have to go beyond the actual energy sector, involving the analysis of other sectors of economy, as well as systems such as banking, budgetary, legislative, tax. Due to the complexity of the discussed problems, the obvious is the need to develop appropriate forecast-analytical system, allowing regional authorities to implement evidence-based predictions of the consequences of management decisions. Multivariant scenario study on development of fuel and energy complex and separately industry, to use the methods of project-based management, harmonized application of state regulation of strategic and market mechanisms on the operational directions of development of fuel and energy complex and separately industry in the economy of the region.

  3. Evaluation of Enhanced High Resolution MODIS/AMSR-E SSTs and the Impact on Regional Weather Forecast

    Science.gov (United States)

    Schiferl, Luke D.; Fuell, Kevin K.; Case, Jonathan L.; Jedlovec, Gary J.

    2010-01-01

    Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instruments as well as the Operational Sea Surface Temperature and Sea Ice Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs

  4. World oil demand's shift toward faster growing and less price-responsive products and regions

    Energy Technology Data Exchange (ETDEWEB)

    Dargay, Joyce M. [Institute for Transport Studies, University of Leeds, Leeds LS2 9JT (United Kingdom); Gately, Dermot [Dept. of Economics, New York University, 19W. 4 St., New York, NY 10012 (United States)

    2010-10-15

    Using data for 1971-2008, we estimate the effects of changes in price and income on world oil demand, disaggregated by product - transport oil, fuel oil (residual and heating oil), and other oil - for six groups of countries. Most of the demand reductions since 1973-74 were due to fuel-switching away from fuel oil, especially in the OECD; in addition, the collapse of the Former Soviet Union (FSU) reduced their oil consumption substantially. Demand for transport and other oil was much less price-responsive, and has grown almost as rapidly as income, especially outside the OECD and FSU. World oil demand has shifted toward products and regions that are faster growing and less price-responsive. In contrast to projections to 2030 of declining per-capita demand for the world as a whole - by the U.S. Department of Energy (DOE), International Energy Agency (IEA) and OPEC - we project modest growth. Our projections for total world demand in 2030 are at least 20% higher than projections by those three institutions, using similar assumptions about income growth and oil prices, because we project rest-of-world growth that is consistent with historical patterns, in contrast to the dramatic slowdowns which they project. (author)

  5. Earthquake forecast for the Wasatch Front region of the Intermountain West

    Science.gov (United States)

    DuRoss, Christopher B.

    2016-04-18

    The Working Group on Utah Earthquake Probabilities has assessed the probability of large earthquakes in the Wasatch Front region. There is a 43 percent probability of one or more magnitude 6.75 or greater earthquakes and a 57 percent probability of one or more magnitude 6.0 or greater earthquakes in the region in the next 50 years. These results highlight the threat of large earthquakes in the region.

  6. Forecast models for urban extreme temperatures : Karachi region as a case study

    International Nuclear Information System (INIS)

    Hussain, M.A.; Abbas, A.; Ansari, M.R.K.

    2010-01-01

    The climatic signature of global warming is both local and global. The forcing by increasing greenhouse gases is global, so there is clearly a global component to the climatic signature. Moreover, the damaging impacts of global warming are manifesting themselves around the world in the form of extreme weather events like storms, tornadoes, floods and droughts, all of which have been escalating in frequency and intensity. Furthermore, it is a well-known fact that there is high degree of uncertainty surrounding projections of basic climate variables, such as temperature and precipitation. However, numerous authors have explored many of these effects individually and have begun exploring the interactions between climate change-induced impacts in different sectors of urban activities. Therefore, it is safe to say that an attempt to conduct a definitive, comprehensive analysis of all the potential impacts of climate change on the urban structure is premature at present. This communication attempts to examine the trends in maximum monthly urban temperature fluctuations. Analysis reveals increasing trends in urban temperature fluctuations showing effect of Karachi industrializations. Forecast models also suggest future scenario with respect to occurrence of extreme temperature. The analysis carried out in this work would be useful for urban planners for sustainable future development, economists and environmentalists etc. (author)

  7. Verificación de los pronosticos del modelo BRAMS centrado en la region subtropical de Sudamerica BRAMS forecast verification over a region of subtropical Southamerica

    Directory of Open Access Journals (Sweden)

    Yanina García Skabar

    2012-09-01

    Full Text Available El objetivo del presente trabajo es realizar una verificación de los pronósticos generados en el Departamento de Ciencias de la Atmósfera y los Océanos de la Universidad de Buenos Aires con el modelo Brazilian Regional Atmospheric Modeling System(BRAMS. Utilizando dicho modelo, desde 2006, se realizan dos pronósticos al día por un plazo de 72 horas en dos retículas anidadas con una resolución horizontal de 80 y 20km. La retícula de mayor resolución abarca Paraguay, Uruguay, Sur de Brasil y Bolivia y el centro y norte de Argentina y Chile. En este trabajo se realiza la verificación sobre el dominio de mayor resolución para el período desde abril de 2006 a diciembre de 2008. El interés se centra en verificar los pronósticos de precipitación, y de los perfiles verticales de temperatura, humedad y viento en las localidades donde se dispone de datos de radiosondeos. La verificación de los pronósticos de precipitación se realiza mediante estadísticos calculados frente a las observaciones convencionales disponibles y frente a estimaciones de precipitación CMORPH. Los resultados son similares a los obtenidos en la región con modelos de aproximadamente la misma resolución. Además se identifican las deficiencias principales de los pronósticos lo que permitirá realizar ajustes en la configuración del mismo para mejorar su desempeño.The aim of this work is to verify the forecasts generated in the Department of Atmospheric Sciences and Ocean, University of Buenos Aires with the Brazilian model Regional Atmospheric Modeling System (BRAMS. Since year 2006, every day two different forecasts that extend for 72 hours have been performed using two nested grids with an horizontal resolution of 80 and 20 km. The higher resolution domain covers Paraguay, Uruguay, southern Brazil and Bolivia and the central and northern part of Argentina and Chile. This paper presents the results of the verification of the model in the higher resolution

  8. Flood forecasting and uncertainty of precipitation forecasts

    International Nuclear Information System (INIS)

    Kobold, Mira; Suselj, Kay

    2004-01-01

    The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution

  9. Taxes, cost and demand shifters as determinants in the regional gasoline price formation process: Evidence from Spain

    International Nuclear Information System (INIS)

    Bello, Alejandro; Contín-Pilart, Ignacio

    2012-01-01

    This paper examines the pass-through of regional tax changes and spot price variations to regional gasoline prices in Spain. It also analyzes the impact of all major cost and demand shifters that contribute to regional gasoline price formation. To address these research issues, a reduced form price equation using monthly time-series cross-sectional (TSCS) data from January 2004 through December 2008 is estimated. Strong and consistent evidence of full shifting of regional tax changes to regional gasoline prices is found. Gasoline spot price changes are more than proportionally passed through to retail prices. In addition, the empirical evidence shows, on the one hand, that regional gasoline price differences before taxes continue to be quite narrow and, on the other hand, that there is still a margin for larger gasoline price differences among regions. This suggest that “traditional practices” from the monopoly era (i.e. relatively uniform regional gasoline prices) persist after the market has been liberalized, which may have been facilitated by the strong and uniform presence of the major Spanish-based refining companies in the retail sector over the whole country. - Highlights: ► The paper analyzes the impact of all major demand and cost shifters that contribute to regional gasoline price formation. ► It shows that the relatively uniform regional gasoline prices persist after the Spanish gasoline market has been liberalized. ► It shows that regional tax changes are fully passed on to regional gasoline prices. ► It also shows that gasoline spot price changes are fully passed on to consumer prices.

  10. Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

    Science.gov (United States)

    Tariku, Tebikachew Betru; Gan, Thian Yew

    2018-06-01

    Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional

  11. Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

    Science.gov (United States)

    Tariku, Tebikachew Betru; Gan, Thian Yew

    2017-08-01

    Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional

  12. Demand-point constrained EMS vehicle allocation problems for regions with both urban and rural areas

    NARCIS (Netherlands)

    M. van Buuren (Martin); R.D. van der Mei (Rob); S. Bhulai (Sandjai)

    2017-01-01

    textabstractGovernments deal with increasing health care demand and costs, while budgets are tightened. At the same time, ambulance providers are expected to deliver high-quality service at affordable cost. Maximum reliability and minimal availability models guarantee a minimal performance level at

  13. Sediment oxygen demand of wetlands in the oil sands region of north-eastern Alberta

    International Nuclear Information System (INIS)

    Slama, C.; Ciborowski, J.J.; Gardner Costa, J.

    2009-01-01

    Reclaimed land in the Alberta oil sands mining area contains both reference and oil sands process-affected wetlands constructed using varying sediment compositions. The sediments derived from oil sands process materials (OSPM) may alter the biochemical reactions that take place and affect the sediment oxygen demand (SOD), which is a key factor that contributes to oxygen depletion. This presentation reported on a study in which SOD was measured in a suite of constructed wetlands of different ages, with or without OSPM and topsoil. The purpose of the study was to clarify the role of SOD in wetland function and in the reclamation process. Dissolved oxygen loggers were inserted into dome-shaped chambers on the sediment to measure changes in oxygen demand. Complementary measurements of respiration (CO 2 elution) were used to quantify the biological sediment oxygen demand (BSOD) component of SOD. The chemical sediment oxygen demand (CSOD) was then determined by subtraction from SOD. Wetlands reclaimed using OSPM are expected to have a lower BSOD to CSOD ratio than reference wetlands. Residual ammonia in OSPM sediments may react with sulphate and bind phosphorus. This reduces phosphorus bioavailability and may impede submergent macrophyte growth. As such, wetlands affected by CSOD will have fewer submerged macrophytes than BSOD dominant wetlands.

  14. Potential climate change impacts on water availability and cooling water demand in the Lusatian Lignite Mining Region, Central Europe

    Science.gov (United States)

    Pohle, Ina; Koch, Hagen; Gädeke, Anne; Grünewald, Uwe; Kaltofen, Michael; Redetzky, Michael

    2014-05-01

    In the catchments of the rivers Schwarze Elster, Spree and Lusatian Neisse, hydrologic and socioeconomic systems are coupled via a complex water management system in which water users, reservoirs and water transfers are included. Lignite mining and electricity production are major water users in the region: To allow for open pit lignite mining, ground water is depleted and released into the river system while cooling water is used in the thermal power plants. In order to assess potential climate change impacts on water availability in the catchments as well as on the water demand of the thermal power plants, a climate change impact assessment was performed using the hydrological model SWIM and the long term water management model WBalMo. The potential impacts of climate change were considered by using three regional climate change scenarios of the statistical regional climate model STAR assuming a further temperature increase of 0, 2 or 3 K by the year 2050 in the region respectively. Furthermore, scenarios assuming decreasing mining activities in terms of a decreasing groundwater depression cone, lower mining water discharges, and reduced cooling water demand of the thermal power plants are considered. In the standard version of the WBalMo model cooling water demand is considered as static with regard to climate variables. However, changes in the future cooling water demand over time according to the plans of the local mining and power plant operator are considered. In order to account for climate change impacts on the cooling water demand of the thermal power plants, a dynamical approach for calculating water demand was implemented in WBalMo. As this approach is based on air temperature and air humidity, the projected air temperature and air humidity of the climate scenarios at the locations of the power plants are included in the calculation. Due to increasing temperature and decreasing precipitation declining natural and managed discharges, and hence a lower

  15. Combined desalination, water reuse, and aquifer storage and recovery to meet water supply demands in the GCC/MENA region

    KAUST Repository

    Ghaffour, Noreddine

    2013-01-01

    Desalination is no longer considered as a nonconventional resource to supply potable water in several countries, especially in the Gulf Corporation Countries (GCC) and Middle East and North Africa (MENA) region as most of the big cities rely almost 100% on desalinated water for their supply. Due to the continuous increase in water demand, more large-scale plants are expected to be constructed in the region. However, most of the large cities in these countries have very limited water storage capacity, ranging from hours to a few days only and their groundwater capacity is very limited. The growing need for fresh water has led to significant cost reduction, because of technological improvements of desalination technologies which makes it an attractive option for water supply even in countries where desalination was unthinkable in the past. In the GCC/MENA region, operating records show that water demand is relatively constant during the year, while power demand varies considerably with a high peak in the summer season. However, desalination and power plants are economically and technically efficient only if they are fully operated at close to full capacity. In addition, desalination plants are exposed to external constraints leading to unexpected shutdowns (e.g. red tides). Hybridization of different technologies, including reverse osmosis and thermal-based plants, is used to balance the power to water mismatch in the demand by using the idle power from co-generation systems during low power demand periods. This has led to consideration of storage of additional desalinated water to allow for maximum production and stability in operation. Aquifer storage and recovery (ASR) would then be a good option to store the surplus of desalinated water which could be used when water demand is high or during unexpected shutdowns of desalination plants. In addition, increased reuse of treated wastewater could bring an integrated approach to water resources management. In this

  16. [Forecast of physician workforce in the 22 French regions (1998-2013)].

    Science.gov (United States)

    Coste, Emmanuel; Doan, Bui Dang Ha

    2003-01-01

    The first chapter provides detailed information on the methodology and data used. As were the other projections carried out in the past by the Centre de sociologie et de démographie médicales, the present work is basically a demographic projection. Whereas a projection of national workforce uses the total number of annual new graduates as inflow, a regional projection has to collect other types of data, as new graduates move freely from one region to another. This fundamental difference implies the use of more complex models for predicting the flows of newcomers at regional level. As concerns the outflow, a single table covering retirement and death is used for all regions. The medical profession is a homogeneous group and such a decision is not irrelevant. The second chapter presents briefly the results of the work as concerns one region. In the high variant, the regional number of physicians will continue to increase from 1998 to 2007 or 2008, then it will decrease. In the low variant, the growth will end in 2004 or 2005, followed by a stronger decreasing process. The decrease is caused by the "graduate boom" cohorts (doctors graduated during the 70's and 80's) reaching retirement age. Given the duration of medical training, the decrease will take place even if the numerous clausus (at the start of medical training) rises steadily right now. The decrease in the number of physicians will be accompanied by an aging process: the proportion of senior active doctors will raise and that of junior doctors will diminish. The third chapter evidences the diversity of the various regional trends.

  17. Modelling the impact of urban form on household energy demand and related CO2 emissions in the Greater Dublin Region

    International Nuclear Information System (INIS)

    Liu Xiaochen; Sweeney, John

    2012-01-01

    This study aims to investigate the relationship between household space heating energy use and urban form (land use characteristics) for the Greater Dublin Region. The geographical distributions of household energy use are evaluated at the Enumeration Districts (ED) level based on the building thermal balance model. Moreover, it estimates the impact of possible factors on the household space heating consumption. Results illustrate that the distribution profile of dwellings is a significant factor related to overall heating energy demand and individual dwelling energy consumption for space heating. Residents living in compact dwellings with small floor areas consume less energy for space heating than residents living in dwellings with big floor areas. Moreover, domestic heating energy demand per household was also estimated for two extreme urban development scenarios: the compact city scenario and the dispersed scenario. The results illustrate that the compact city scenario is likely to decrease the domestic heating energy consumption per household by 16.2% compared with the dispersed city scenario. Correspondingly, the energy-related CO 2 emissions could be significantly decreased by compact city scenario compared with the dispersed city scenario. - Highlights: ► A method was developed to investigate urban form impacts on energy demand. ► This study estimates impacts of possible factors on the household energy consumption. ► Household heating energy demand is sensitive to dwelling distribution profile. ► The compact case could reduce domestic energy demand compared with the dispersed case.

  18. Modeling framework for estimating impacts of climate change on electricity demand at regional level: Case of Greece

    International Nuclear Information System (INIS)

    Mirasgedis, S.; Sarafidis, Y.; Georgopoulou, E.; Kotroni, V.; Lagouvardos, K.; Lalas, D.P.

    2007-01-01

    This paper focuses on the potential upcoming impacts of climate change in the 21st century on electricity demand at regional/national levels for regions where topography and location result in large differences in local climate. To address this issue, a regional climate model, PRECIS, has been used to predict future climatic conditions under different emissions scenarios (namely A2 and B2 of the IPCC special report on emissions scenarios (SRES)) as an input to a multiple regression model of the sensitivity of electricity demand in the Greek interconnected power system to climate and socio-economic factors. The economic development input to the multiple regression model follows the same storylines of the SRES scenarios upto 2100 and includes sub-scenarios to cover larger and smaller economic development rates. The results of the analysis indicate an increase of the annual electricity demand attributable solely to climate change of 3.6-5.5% under all scenarios examined, most of which results from increased annual variability with substantial increases during the summer period that outweighs moderate declines estimated for the winter period. This becomes more pronounced if inter-annual variability, especially of summer months, is taken into consideration. It was also found that in the long run, economic development will have a strong effect on future electricity demand, thus increasing substantially the total amount of energy consumed for cooling and heating purposes. This substantial increase in energy demand with strong annual variability will lead to the need for inordinate increases of installed capacity, a large percentage of which will be under utilized. Thus, appropriate adaptation strategies (e.g. new investments, interconnections with other power systems, energy saving programmes, etc.) need to be developed at the state level in order to ensure the security of energy supply. (author)

  19. Forecast simulation of rapidly-intensified typhoon in the Eddy-Rich Northwest Pacific region

    Science.gov (United States)

    Kim, Kyeong Ok; Yuk, Jin-Hee; Jung, Kyung Tae; Kuh Kang, Suk

    2017-04-01

    The real-time typhoon predictions in the Northwest Pacific (NWP) are being distributed by various agencies (for example, KMA, JMA, JTWC, NMC, CWB, HKO and PAGASA). Currently the movement of the typhoon can be predicted with an error of less than 100 km in 48 hours, however it is difficult to the predict of the intensity of the typhoon especially the Rapidly Intensified (RI) Typhoons. The mean occurrence of RI typhoon amounts to 5.4 times a year during 39 years (1977-2015), occupying 21% of typhoons in NWP. Especially the RI typhoon in the Eddy-Rich Northwest Pacific (ER-NWP) occurred 1.8 times a year, covering 29% of typhoons in ER-NWP. A RI typhoon, NEPARTAK (T201601), occurred in July 2016. It was formed in Caroline Islands and moved northwest, straightly heading for Taiwan. However, at the beginning stage many forecasting agencies predicts as move to the Yellow Sea. The accuracy of prediction data of the Typhoon NEPARTAK (T201601) from KMA, JMA and JTWC was compared with the adjusted best-track data from Digital-Typhoon (JMA-RSMC). The sequential prediction data are summarized with 6-hour interval from 3th to 10th July 2016.The JMA prediction of the typhoon track and the JTWC predictions of the maximum wind speed were found to be best. The numerical simulations using WRF model forced with NCEP GFS prediction data and microwave SST is compared. The simulations using one domain (D1), two domains (D2) using a moving nest scheme, and with or without the spectral nudging (-SN) are compared. Comparison of the errors on the track shows the differences of 100 km in 48-hour prediction and200 km in 72-hour prediction on average. The best results on the track prediction are shown in the D2 case of WRF model. However, underestimation of the maximum wind speed of WRF prediction still exists, obviously requiring better understanding of RI-related processes to improve the model prediction.

  20. Determining the best forecasting method to estimate unitary charges price indexes of PFI data in central region Peninsular Malaysia

    Science.gov (United States)

    Ahmad Kamaruddin, Saadi Bin; Md Ghani, Nor Azura; Mohamed Ramli, Norazan

    2013-04-01

    The concept of Private Financial Initiative (PFI) has been implemented by many developed countries as an innovative way for the governments to improve future public service delivery and infrastructure procurement. However, the idea is just about to germinate in Malaysia and its success is still vague. The major phase that needs to be given main attention in this agenda is value for money whereby optimum efficiency and effectiveness of each expense is attained. Therefore, at the early stage of this study, estimating unitary charges or materials price indexes in each region in Malaysia was the key objective. This particular study aims to discover the best forecasting method to estimate unitary charges price indexes in construction industry by different regions in the central region of Peninsular Malaysia (Selangor, Federal Territory of Kuala Lumpur, Negeri Sembilan, and Melaka). The unitary charges indexes data used were from year 2002 to 2011 monthly data of different states in the central region Peninsular Malaysia, comprising price indexes of aggregate, sand, steel reinforcement, ready mix concrete, bricks and partition, roof material, floor and wall finishes, ceiling, plumbing materials, sanitary fittings, paint, glass, steel and metal sections, timber and plywood. At the end of the study, it was found that Backpropagation Neural Network with linear transfer function produced the most accurate and reliable results for estimating unitary charges price indexes in every states in central region Peninsular Malaysia based on the Root Mean Squared Errors, where the values for both estimation and evaluation sets were approximately zero and highly significant at p Malaysia. The estimated price indexes of construction materials will contribute significantly to the value for money of PFI as well as towards Malaysian economical growth.

  1. Prevendo a demanda de ligações em um call center por meio de um modelo de Regressão Múltipla Forecasting a call center demand using a Multiple Regression model

    Directory of Open Access Journals (Sweden)

    Marco Aurélio Carino Bouzada

    2009-09-01

    Full Text Available Este trabalho descreve - por meio do estudo de um caso - o problema da previsão de demanda de chamadas para um determinado produto no call center de uma grande empresa brasileira do setor - a Contax - e como ele foi abordado com o uso de Regressão Múltipla com variáveis dummy. Depois de destacar e justificar a importância do tema, o estudo apresenta uma breve revisão de literatura acerca de métodos de previsão de demanda e de sua aplicação em call centers. O caso é descrito, contextualizando, inicialmente, a empresa estudada e descrevendo, a seguir, a forma como ela lida com o problema de previsão de demanda de chamadas para o produto 103 - serviços relacionados à telefonia fixa. Um modelo de Regressão Múltipla com variáveis dummy é, então, desenvolvido para servir como base do processo de previsão de demanda proposto. Este modelo utiliza informações disponíveis capazes de influenciar a demanda, tais como o dia da semana, a ocorrência ou não de feriado e a proximidade da data com eventos críticos, como a chegada da conta à residência do cliente e seu vencimento; e apresentou ganhos de acurácia da ordem de 3 pontos percentuais para o período estudado, quando comparado com a ferramenta anteriormente em uso.This work describes - with the aid of a case study -a demand forecast problem for a specific product reported to the call center of a large Brazilian company in an industry called Contax, and the way it was approached with the use of Multiple Regression using dummy variables. After highlighting and justifying the studied matter relevance, the article presents a small literature review regarding demand forecast methods and their use in the call center industry. The case is described presenting the studied company and the way it deals with the Forecasting Demand for a telephone all center regarding telephone services products. Therefore, a Multiple Regression with dummy variables model was developed to work as the

  2. System learning approach to assess sustainability and forecast trends in regional dynamics: The San Luis Basin study, Colorado, U.S.A.

    Science.gov (United States)

    This paper presents a methodology that combines the power of an Artificial Neural Network and Information Theory to forecast variables describing the condition of a regional system. The novelty and strength of this approach is in the application of Fisher information, a key metho...

  3. Climatology and trend of wind power resources in China and its surrounding regions: a revisit using Climate Forecast System Reanalysis data

    Science.gov (United States)

    Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

    2015-01-01

    The mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33-year (1979–2011) wind data from the Climate Forecast System Reanalysis (CFSR) that has many improvements including higher spatial resolution over previous global reanalysis...

  4. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region

    Science.gov (United States)

    He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

    2014-02-01

    Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.

  5. Forecasting Food Supply Chain Developments in Lagging Rural Regions: Evidence from the UK