WorldWideScience

Sample records for regional demand forecasts

  1. Demand forecasting

    OpenAIRE

    Gregor, Belčec

    2011-01-01

    Companies operate in an increasingly challenging environment that requires them to continuously improve all areas of the business process. Demand forecasting is one area in manufacturing companies where we can hope to gain great advantages. Improvements in forecasting can result in cost savings throughout the supply chain, improve the reliability of information and the quality of the service for our customers. In the company Danfoss Trata, d. o. o. we did not have a system for demand forecast...

  2. U.S. Regional Demand Forecasts Using NEMS and GIS

    Energy Technology Data Exchange (ETDEWEB)

    Cohen, Jesse A.; Edwards, Jennifer L.; Marnay, Chris

    2005-07-01

    The National Energy Modeling System (NEMS) is a multi-sector, integrated model of the U.S. energy system put out by the Department of Energy's Energy Information Administration. NEMS is used to produce the annual 20-year forecast of U.S. energy use aggregated to the nine-region census division level. The research objective was to disaggregate this regional energy forecast to the county level for select forecast years, for use in a more detailed and accurate regional analysis of energy usage across the U.S. The process of disaggregation using a geographic information system (GIS) was researched and a model was created utilizing available population forecasts and climate zone data. The model's primary purpose was to generate an energy demand forecast with greater spatial resolution than what is currently produced by NEMS, and to produce a flexible model that can be used repeatedly as an add-on to NEMS in which detailed analysis can be executed exogenously with results fed back into the NEMS data flow. The methods developed were then applied to the study data to obtain residential and commercial electricity demand forecasts. The model was subjected to comparative and statistical testing to assess predictive accuracy. Forecasts using this model were robust and accurate in slow-growing, temperate regions such as the Midwest and Mountain regions. Interestingly, however, the model performed with less accuracy in the Pacific and Northwest regions of the country where population growth was more active. In the future more refined methods will be necessary to improve the accuracy of these forecasts. The disaggregation method was written into a flexible tool within the ArcGIS environment which enables the user to output the results in five year intervals over the period 2000-2025. In addition, the outputs of this tool were used to develop a time-series simulation showing the temporal changes in electricity forecasts in terms of absolute, per capita, and density of

  3. Demand Forecasting Errors

    OpenAIRE

    Mackie, Peter; Nellthorp, John; Laird, James

    2005-01-01

    Demand forecasts form a key input to the economic appraisal. As such any errors present within the demand forecasts will undermine the reliability of the economic appraisal. The minimization of demand forecasting errors is therefore important in the delivery of a robust appraisal. This issue is addressed in this note by introducing the key issues, and error types present within demand fore...

  4. Development of golf tourism and golf tourism demand forecasts in Turkey: a study of Belek region

    Directory of Open Access Journals (Sweden)

    Murat Çuhadar

    2013-06-01

    Full Text Available Golf tourism has become one of the rapidly developing tourism types in Turkey, especially in the Belek region. In this study, detailed information about the development of golf tourism in Turkey from past to present was provided and golf tourism demand to Belek region which is a major golf tourism destinastion in the world and Turkey was modeled and forecasted monthly by Box-Jenkins methodology for the May 2013 –December 2014 period. As a measure of golf tourism demand, number of monthly golf games were taken in the study and the monthly number of golf game statistics of January 2001 – April 2013 in the golf establishments in Belek tourism center were used. By producing ex-ante forecasts it is aimed to create a basis for tourism development plans prepared by the management of private and public sector and to provide support for administrators’ monthly planning decisions.

  5. Intelligent energy demand forecasting

    CERN Document Server

    Hong, Wei-Chiang

    2013-01-01

    This book offers approaches and methods to calculate optimal electric energy allocation, using evolutionary algorithms and intelligent analytical tools to improve the accuracy of demand forecasting. Focuses on improving the drawbacks of existing algorithms.

  6. 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.

  7. Are demand forecasting techniques applicable to libraries?

    OpenAIRE

    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.

  8. Are demand forecasting techniques applicable to libraries?

    OpenAIRE

    M S Sridhar

    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.

  9. 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.

  10. 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

  11. Forecasting Daily Demand in Cash Supply Chains

    National Research Council Canada - National Science Library

    Michael Wagner

    2010-01-01

    ...: This study contrasted competing techniques of forecasting daily demand in cash supply chains in order to determine the overall performance and the potential of joint forecasting for integrated planning...

  12. Genetic algorithms in seasonal demand forecasting

    OpenAIRE

    Chodak, Grzegorz; Kwaśnicki, Witold

    2000-01-01

    The method of forecasting seasonal demand applying genetic algorithm is presented. Specific form of used demand function is shown in the first section of the article. Next the method of identification of the function parameters using genetic algorithms is discussed. In the final section an example of applying proposed method to forecast real demand process is shown.

  13. Measuring inaccuracy in travel demand forecasting

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent

    2005-01-01

    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...... 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...... in travel demand forecasts are likely to be conservatively biased, i.e., accuracy in travel demand forecasts estimated from such samples would likely be higher than accuracy in travel demand forecasts in the project population. This bias must be taken into account when interpreting the results from...

  14. 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.

  15. Forecasting and Analysis of Agricultural Product Logistics Demand in Tibet Based on Combination Forecasting Model

    Institute of Scientific and Technical Information of China (English)

    Wenfeng; YANG

    2015-01-01

    Over the years,the logistics development in Tibet has fallen behind the transport. Since the opening of Qinghai-Tibet Railway in2006,the opportunity for development of modern logistics has been brought to Tibet. The logistics demand analysis and forecasting is a prerequisite for regional logistics planning. By establishing indicator system for logistics demand of agricultural products,agricultural product logistics principal component regression model,gray forecasting model,BP neural network forecasting model are built. Because of the single model’s limitations,quadratic-linear programming model is used to build combination forecasting model to predict the logistics demand scale of agricultural products in Tibet over the next five years. The empirical analysis results show that combination forecasting model is superior to single forecasting model,and it has higher precision,so combination forecasting model will have much wider application foreground and development potential in the field of logistics.

  16. Surveys and forecasting industrial property demand

    OpenAIRE

    2011-01-01

    The aim of this paper is to increase the knowledge about industrial land and floor space forecasting. From the rare research on this subject it is known that the knowledge about factors that influence demand and techniques that model demand is not available in abundance. However, different kinds of models are used extensively in practice. This paper focuses the difference between of stated and revealed preferences. In planning practices stated preferences are often used to forecast land and f...

  17. Accuracy analysis of TDRSS demand forecasts

    Science.gov (United States)

    Stern, Daniel C.; Levine, Allen J.; Pitt, Karl J.

    1994-01-01

    This paper reviews Space Network (SN) demand forecasting experience over the past 16 years and describes methods used in the forecasts. The paper focuses on the Single Access (SA) service, the most sought-after resource in the Space Network. Of the ten years of actual demand data available, only the last five years (1989 to 1993) were considered predictive due to the extensive impact of the Challenger accident of 1986. NASA's Space Network provides tracking and communications services to user spacecraft such as the Shuttle and the Hubble Space Telescope. Forecasting the customer requirements is essential to planning network resources and to establishing service commitments to future customers. The lead time to procure Tracking and Data Relay Satellites (TDRS's) requires demand forecasts ten years in the future a planning horizon beyond the funding commitments for missions to be supported. The long range forecasts are shown to have had a bias toward underestimation in the 1991 -1992 period. The trend of underestimation can be expected to be replaced by overestimation for a number of years starting with 1998. At that time demand from new missions slated for launch will be larger than the demand from ongoing missions, making the potential for delay the dominant factor. If the new missions appear as scheduled, the forecasts are likely to be moderately underestimated. The SN commitment to meet the negotiated customer's requirements calls for conservatism in the forecasting. Modification of the forecasting procedure to account for a delay bias is, therefore, not advised. Fine tuning the mission model to more accurately reflect the current actual demand is recommended as it may marginally improve the first year forecasting.

  18. 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...

  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. Study of forecasting technique of power demand

    Energy Technology Data Exchange (ETDEWEB)

    Jung, T.Y. [Korea Energy Economics Institute, Euiwang (Korea, Republic of)

    1998-04-01

    Long-term forecast of power and energy demand become an important base of investment plan of the energy supply sector, and a study based on value data is indispensable since the expansion of energy supply requires a long construction period and a lot of investment capital. Total estimated power and energy demand of the whole economy is known to have considerable relationship with macroeconomics factors. This study reviews the Kalman filter technique and abnormal time series analysis technique which is useful in analyzing energy and macroeconomics data taking the form of pre-tallied time series data. Based on these, power demand and energy demand are estimated using real data and the most reliable technique, and long-term forecast of power and energy demand is tried up to year 2015 based on the forecasted values. It should be noted that power and energy demand of Korea shows the structurally-changing behavior based on the present while the forecast based on past data involves an assumption naturally that the trend continues from the past to the present. 15 refs., 8 figs., 15 tabs.

  1. Demand forecasting and information platform in tourism

    Science.gov (United States)

    Li, Yue; Jiang, Qi-Jie

    2017-05-01

    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. DEMAND CATEGORISATION, FORECASTING, AND INVENTORY CONTROL FOR INTERMITTENT DEMAND ITEMS

    Directory of Open Access Journals (Sweden)

    E. Babiloni

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: It is commonly assumed that intermittent demand appears randomly, with many periods without demand; but that when it does appear, it tends to be higher than unit size. Basic and well-known forecasting techniques and stock policies perform very poorly with intermittent demand, making new approaches necessary. To select the appropriate inventory management policy, it is important to understand the demand pattern for the items, especially when demand is intermittent. The use of a forecasting method designed for an intermittent demand pattern, such as Croston’s method, is required instead of a simpler and more common approach such as exponential smoothing. The starting point is to establish taxonomic rules to select efficiently the most appropriate forecasting and stock control policy to cope with thousands of items found in real environments. This paper contributes to the state of the art in: (i categorisation of the demand pattern; (ii methods to forecast intermittent demand; and (iii stock control methods for items with intermittent demand patterns. The paper first presents a structured literature review to introduce managers to the theoretical research about how to deal with intermittent demand items in both forecasting and stock control methods, and then it points out some research gaps for future development for the three topics.

    AFRIKAANSE OPSOMMING: Daar word algemeen aanvaar dat intermitterende vraag op toevalswyse voorkom, met verskeie periodes waar daar geen vraag is nie. Wanneer die vraag dan wel materialiseer, oorskry dit dikwels die eenheidsgrootte. Die bekende vooruitskattingstegnieke en voorraadbeleidstellings het min sukses waar intermitterende vraag voorkom, sodat nuwe benaderings nodig is om die problem aan te spreek. Om ‘n geskikte voorraadbestuur-beleid te selekteer, is dit noodsaaklik om die vraagpatroon van die items te verstaan, juis in gevalle van intermitterende patrone. Die gebruik van

  3. 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

  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. WOD - Weather On Demand forecasting system

    Science.gov (United States)

    Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

    2017-04-01

    The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.

  6. 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.

  7. Intermittent Demand Forecasting in a Tertiary Pediatric Intensive Care Unit.

    Science.gov (United States)

    Cheng, Chen-Yang; Chiang, Kuo-Liang; Chen, Meng-Yin

    2016-10-01

    Forecasts of the demand for medical supplies both directly and indirectly affect the operating costs and the quality of the care provided by health care institutions. Specifically, overestimating demand induces an inventory surplus, whereas underestimating demand possibly compromises patient safety. Uncertainty in forecasting the consumption of medical supplies generates intermittent demand events. The intermittent demand patterns for medical supplies are generally classified as lumpy, erratic, smooth, and slow-moving demand. This study was conducted with the purpose of advancing a tertiary pediatric intensive care unit's efforts to achieve a high level of accuracy in its forecasting of the demand for medical supplies. On this point, several demand forecasting methods were compared in terms of the forecast accuracy of each. The results confirm that applying Croston's method combined with a single exponential smoothing method yields the most accurate results for forecasting lumpy, erratic, and slow-moving demand, whereas the Simple Moving Average (SMA) method is the most suitable for forecasting smooth demand. In addition, when the classification of demand consumption patterns were combined with the demand forecasting models, the forecasting errors were minimized, indicating that this classification framework can play a role in improving patient safety and reducing inventory management costs in health care institutions.

  8. Improving the Model for Energy Consumption Load Demand Forecasting

    Science.gov (United States)

    Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

    This paper proposes an application of a filter method in preprocessing stage for mid-term load demand forecasting to improve electricity load forecasting and to guarantee satisfactory forecasting accuracy. Case study employs the historical electricity consumption demand data in Thailand which were recorded in the 12 years of 1997 through to 2007. The load demand forecasted value is used for unit commitment and fuel reserve planning in the power system. This method consists of a trend component and a cyclical component decomposed from the original load demand using the Hodrick-Prescott (HP) filter in the preprocessing stage and the forecasting of each component using Double Neural Networks (DNNs) in the forecasting stage. Experimental results show that with preprocessing before forecasting can predict the load demand better than that without preprocessing.

  9. 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.

  10. Modelling and forecasting Turkish residential electricity demand

    Energy Technology Data Exchange (ETDEWEB)

    Dilaver, Zafer, E-mail: Z.dilaver@surrey.ac.uk [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom); The Republic of Turkey Prime Ministry, PK 06573, Ankara (Turkey); Hunt, Lester C [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom)

    2011-06-15

    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.

  11. Using Seasonal Forecasts for medium-term Electricity Demand Forecasting on Italy

    Science.gov (United States)

    De Felice, M.; Alessandri, A.; Ruti, P.

    2012-12-01

    the performances of electricity demand forecast performed with predicted variables on Italian regions with encouraging results on the South of Italy. This work gives an initial assessment on the predictability of electricity demand on seasonal time scale, evaluating the relevance of climate information provided by seasonal forecasts for electricity management during high-demand periods.;

  12. Ex-post evaluations of demand forecast accuracy

    DEFF Research Database (Denmark)

    Nicolaisen, Morten Skou; Driscoll, Patrick Arthur

    2014-01-01

    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...

  13. Incorporating weather uncertainty in demand forecasts for electricity market planning

    Science.gov (United States)

    Ziser, C. J.; Dong, Z. Y.; Wong, K. P.

    2012-07-01

    A major component of electricity network planning is to ensure supply capability into the future, through generation and transmission development. Accurate forecasts of maximum demand are a crucial component of this process, with future weather conditions having a large impact on forecast accuracy. This article presents an improved methodology for the consideration of weather uncertainty in electricity demand forecasts. Case studies based on the Australian national electricity market are used to validate the proposed methodology.

  14. 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.

  15. Real-time demand forecasting in the emergency department.

    Science.gov (United States)

    Jones, Spencer S

    2007-10-11

    Shifts in the supply of and demand for emergency department (ED) services have led to ED overcrowding and make the efficient allocation of ED resources increasingly important. Reliable means of modeling and forecasting the demand for resources are critical to any ED resource planning strategy. Vector Autoregression (VAR) is a flexible multivariate time-series forecasting methodology that is well suited to modeling demand for resources in the ED.

  16. 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.

  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. 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...

  19. 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.

  20. 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.

  1. Demand Forecasting in the Fashion Industry: A Review

    OpenAIRE

    Maria Elena Nenni; Luca Giustiniano; Luca Pirolo

    2013-01-01

    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.

  2. Probability Forecast of Regional Landslide Based on Numerical Weather Forecast

    Institute of Scientific and Technical Information of China (English)

    GAO Kechang; WEI Fangqiang; CUI Peng; HU Kaiheng; XU Jing; ZHANG Guoping; BI Baogui

    2006-01-01

    The regional forecast of landslide is one of the key points of hazard mitigation. It is also a hot and difficult point in research field. To solve this problem has become urgent task along with Chinese economy fast development. This paper analyzes the principle of regional landslide forecast and the factors for forecasting. The method of a combination of Information Value Model and Extension Model has been put forward to be as the forecast model. Using new result of Numerical Weather Forecast Research and that combination model, we discuss the implementation feasibility of regional landslide forecast. Finally, with the help of Geographic Information System, an operation system for southwest of China landslide forecast has been developed. It can carry out regional landslide forecast daily and has been pilot run in NMC. Since this is the first time linking theoretical research with meteorological service, further works are needed to enhance it.

  3. Crowd Sourcing Approach for UAS Communication Resource Demand Forecasting

    Science.gov (United States)

    Wargo, Chris A.; Difelici, John; Roy, Aloke; Glaneuski, Jason; Kerczewski, Robert J.

    2016-01-01

    Congressional attention to Unmanned Aircraft Systems (UAS) has caused the Federal Aviation Administration (FAA) to move the National Airspace System (NAS) Integration project forward, but using guidelines, practices and procedures that are yet to be fully integrated with the FAA Aviation Management System. The real drive for change in the NAS will to come from both UAS operators and the government jointly seeing an accurate forecast of UAS usage demand data. This solid forecast information would truly get the attention of planners. This requires not an aggregate demand, but rather a picture of how the demand is spread across small to large UAS, how it is spread across a wide range of missions, how it is expected over time and where, in terms of geospatial locations, will the demand appear. In 2012 the Volpe Center performed a study of the overall future demand for UAS. This was done by aggregate classes of aircraft types. However, the realistic expected demand will appear in clusters of aircraft activities grouped by similar missions on a smaller geographical footprint and then growing from those small cells. In general, there is not a demand forecast that is tightly coupled to the real purpose of the mission requirements (e.g. in terms of real locations and physical structures such as wind mills to inspect, farms to survey, pipelines to patrol, etc.). Being able to present a solid basis for the demand is crucial to getting the attention of investment, government and other fiscal planners. To this end, Mosaic ATM under NASA guidance is developing a crowd sourced, demand forecast engine that can draw forecast details from commercial and government users and vendors. These forecasts will be vetted by a governance panel and then provide for a sharable accurate set of projection data. Our paper describes the project and the technical approach we are using to design and create access for users to the forecast system.

  4. The study on energy demand forecast of Chongqing

    Institute of Scientific and Technical Information of China (English)

    YANGJia; WUXiangsheng

    2003-01-01

    Energy demand forecasting is the base for programming energy system. With the economy development, the increasing amount of energy consumption is in contradiction with the exhausted resource and destroyed environment. For the sake of the sustainable development, the reasonable energy demand forecasting is needed, and it is the scientific reference for establishing energy developing stratagem and energy policy. But the traditional forecasting methods at present have some Jimitations. Artificial neural network(ANN) is a new and developing subject, whose capability of adaptability and self-study is excellent. Based on the analysis as above, the forecasting model with artificial neural network was established. This model is based on the idea of time series. Compared with the traditional algorithm, this model is more friendly and maneuverability, which is not only able to forecast for a short term, but also able to forecast for a long period. Based on the fact of Chongqing, the energy demand of Chongqing is analyzed, and then it is forecasted from 2002 to 2020 with the model, and the forecasting results are reasonable.

  5. Forecasting Demand for Weapon System Items

    Science.gov (United States)

    1994-07-01

    level at quarter n. SL(n) was set using the Presutti- Trepp model that DLA currently uses. Forecast error is required by the model to set the safety level...of inventory investment versus response time, we varied the safety level by changing the "lambda factor" or backorder cost used in the Presutti- Trepp

  6. Global Tungsten Demand and Supply Forecast

    Science.gov (United States)

    Dvořáček, Jaroslav; Sousedíková, Radmila; Vrátný, Tomáš; Jureková, Zdenka

    2017-03-01

    An estimate of the world tungsten demand and supply until 2018 has been made. The figures were obtained by extrapolating from past trends of tungsten production from1905, and its demand from 1964. In addition, estimate suggestions of major production and investment companies were taken into account with regard to implementations of new projects for mining of tungsten or possible termination of its standing extraction. It can be assumed that tungsten supply will match demand by 2018. This suggestion is conditioned by successful implementation of new tungsten extraction projects, and full application of tungsten recycling methods.

  7. Incorporating demand uncertainty and forecasting in supply chain planning models

    OpenAIRE

    Fildes, R A; Kingsman, B G

    2011-01-01

    This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of pract...

  8. NEW CAR DEMAND MODELING AND FORECASTING USING BASS DIFFUSION MODEL

    Directory of Open Access Journals (Sweden)

    Zuhaimy Ismail

    2013-01-01

    Full Text Available Forecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. The building of Bass diffusion model for forecasting new product within the Malaysian society is presented in this study. The proposed model represents the spread level of new Proton car among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new car. With the limited amount of data available for the new car, a robust Bass model was developed to forecast the sales volume. A procedure of the proposed diffusion model was designed and the parameters were estimated. Results obtained by applying the proposed model and numerical calculation shows that the proposed diffusion model is robust and effective for forecasting demand of new Proton car. The proposed diffusion model is shown to forecast more effectively and accurately even with insufficient previous data on the new product.

  9. Forecasting the manpower demand for quantity surveyors in Hong Kong

    Directory of Open Access Journals (Sweden)

    Paul H K Ho

    2013-09-01

    Full Text Available Recently, there has been a massive infrastructure development and an increasing demand for public and private housing, resulting in a shortage of qualified quantity surveyors. This study aims to forecast the demand for qualified quantity surveyors in Hong Kong from 2013 to 2015. Literature review indicates that the demand for quantity surveyors is a function of the gross values of building, civil engineering and maintenance works. The proposed forecasting method consists of two steps. The first step is to estimate the gross values of building, civil engineering and maintenance works by time series methods and the second step is to forecast the manpower demand for quantity surveyors by causal methods. The data for quantity surveyors and construction outputs are based on the ‘manpower survey reports of the building and civil engineering industry’ and the ‘gross value of construction works performed by main contractors’ respectively. The forecasted manpower demand for quantity surveyors in 2013, 2014 and 2015 are 2,480, 2,632 and 2,804 respectively. Due to the low passing rate of the assessment of professional competence (APC and the increasing number of retired qualified members, there will be a serious shortage of qualified quantity surveyors in the coming three years.

  10. Forecasting the manpower demand for quantity surveyors in Hong Kong

    Directory of Open Access Journals (Sweden)

    Paul H K Ho

    2013-09-01

    Full Text Available Recently, there has been a massive infrastructure development and an increasing demand for public and private housing, resulting in a shortage of qualified quantity surveyors. This study aims to forecast the demand for qualified quantity surveyors in Hong Kong from 2013 to 2015. Literature review indicates that the demand for quantity surveyors is a function of the gross values of building, civil engineering and maintenance works. The proposed forecasting method consists of two steps. The first step is to estimate the gross values of building, civil engineering and maintenance works by time series methods and the second step is to forecast the manpower demand for quantity surveyors by causal methods. The data for quantity surveyors and construction outputs are based on the ‘manpower survey reports of the building and civil engineering industry’ and the ‘gross value of construction works performed by main contractors’ respectively. The forecasted manpower demand for quantity surveyors in 2013, 2014 and 2015 are 2,480, 2,632 and 2,804 respectively. Due to the low passing rate of the assessment of professional competence (APC and the increasing number of retired qualified members, there will be a serious shortage of qualified quantity surveyors in the coming three years.

  11. 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.

  12. Demand Forecast Using Data Analytics for the Preallocation of Ambulances.

    Science.gov (United States)

    Chen, Albert Y; Lu, Tsung-Yu; Ma, Matthew Huei-Ming; Sun, Wei-Zen

    2016-07-01

    The objective of prehospital emergency medical services (EMSs) is to have a short response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The geographic information system (GIS) is introduced in this study to manage and visualize the spatial distribution of demand data and forecasting results. A flexible model is implemented in GIS, through which training data are prepared with user-desired sizes for the spatial grid and discretized temporal steps. We applied moving average, artificial neural network, sinusoidal regression, and support vector regression for the forecasting of prehospital emergency medical demand. The results from these approaches, as a reference, could be used for the preallocation of ambulances. A case study is conducted for the EMS in New Taipei City, where prehospital EMS data have been collected for three years. The model selection process has chosen different models with different input features for the forecast of different areas. The best daily mean absolute percentage error during testing of the EMS demand forecast is 23.01%, which is a reasonable forecast based on Lewis' definition. With the acceptable prediction performance, the proposed approach has its potential to be applied to the current practice.

  13. 77 FR 62595 - 30-Day Notice of Proposed Information Collection: Passport Demand Forecasting Study

    Science.gov (United States)

    2012-10-15

    ... Collection: Passport Demand Forecasting Study. OMB Control Number: 1405-0177. Type of Request: Reinstatement... Notice of Proposed Information Collection: Passport Demand Forecasting Study ACTION: Notice of request... data gathered from the Passport Demand Forecasting Study will be used to monitor, assess, and forecast...

  14. 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...

  15. 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.

  16. Disaggregating residential water demand for improved forecasts and decision making

    Science.gov (United States)

    Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

    2003-04-01

    Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water

  17. Development of Ensemble Model Based Water Demand Forecasting Model

    Science.gov (United States)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  18. Analysis and Forecast for Timber Supply and Demand in China

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Based on the data from 2002 to 2010, the paper analyzed the situation of timber supply and demand in China, and concluded that supply-demand could be balanced basically if taking accounting of timber import. Based on the data from the Seventh National Forestry Inventory, the potential of providing timber from natural forest and plantation was analyzed. The paper also forecasted the future features and trend of timber supply and demand in China. In the end, strategic measures and technological and policy gua...

  19. 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.

  20. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  1. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  2. 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.

  3. 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.

  4. 77 FR 40936 - 60-Day Notice of Proposed Information Collection: Passport Demand Forecasting Study Phase III

    Science.gov (United States)

    2012-07-11

    ...: Passport Demand Forecasting Study Phase III. OMB Control Number: None. Type of Request: Reinstatement of a... Notice of Proposed Information Collection: Passport Demand Forecasting Study Phase III ACTION: Notice of.... The data gathered from the Passport Demand Forecasting Study Phase III will be used to monitor, assess...

  5. Optimization of Evaporative Demand Models for Seasonal Drought Forecasting

    Science.gov (United States)

    McEvoy, D.; Huntington, J. L.; Hobbins, M.

    2015-12-01

    Providing reliable seasonal drought forecasts continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) forecasts beyond weather time scales are largely unreliable, so exploring new avenues to improve seasonal drought prediction is necessary to move towards applications and decision-making based on seasonal forecasts. A recent study has shown that evaporative demand (E0) anomaly forecasts from the Climate Forecast System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly forecasts during drought events over CONUS, and E0 drought forecasts may be particularly useful during the growing season in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, forecasts of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and season one forecasts; which are

  6. 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

  7. 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.

  8. 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

    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...

  9. Improved grey-based approach for power demand forecasting

    Institute of Scientific and Technical Information of China (English)

    LIN Jia-mu; LIU Dan

    2006-01-01

    Grey theory is a multidisciplinary and generic theory to cope with systems of poor or deficient information. We proposed in this paper an improved grey method (GM) to overcome the disadvantages of the general GM(1,1). In the improved GM(1,1), a new background value formula is deduced and Markov-chain sign estimation is imbedded into the residual modification model. We tested the efficiency and accuracy of our model by applying it to the power demand forecasting in Taiwan. Experimental results demonstrate the new method has obviously a higher prediction accuracy than the general model.

  10. 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.

  11. Prediction by simulation. Part 3. Forecaste for electric power demand; Denryoku juyo yosoku

    Energy Technology Data Exchange (ETDEWEB)

    Haida, T. [Tokyo Electric Co. Ltd. (Japan)

    1997-08-20

    Electric power demand forecast can be divided into short-range demand forecasting and long-range demand forecasting. A discussion is made placing emphasis on the forecast of the maximum powers for the day and for the following day which are considered to be most important in the short-range demand forecasting. The regression model is a method which persons in charge of forecasting can comprehend easily because it agrees fairly well with their experience and institution. Applications of time series model to demand forecasting for every hour for the following day and for the week are reported. Many attempts are being made recently to use neural network for the forecasting model. For the estimation of the maximum power, meteorological conditions of the day of forecasting are indispensable. As a result, the ultimate accuracy of forecasting is influenced greatly by the accuracy of the forecasted weather. Every electric power company in Japan has a maximum power forecasting support system at the present time. For long-range demand forecasting for a few years and for ten-odd years, macro-methods and micro-methods of employing accumulated demands for applications such as electric lights and electricity for business use are adopted. 11 refs., 6 figs.

  12. Electric Power Demand Forecasting: A Case Study of Lucknow City

    Directory of Open Access Journals (Sweden)

    A.K. Bhardwaj and R.C. Bansal

    2011-03-01

    Full Text Available The study of forecasting identifies the urgent need for special attention in evolving effective energy policies to alleviate an energy famine in the near future. Since power demand is increasing day by day in entire world and it is also one of the fundamental infrastructure input for the development, its prospects and availability sets significant constraints on the socio-economic growth of every person as well as every country. A care full long-term power plan is imperative for the development of power sector. This need assumes more importance in the state of Uttar Pradesh where the demand for electrical energy is growing at a rapid pace. This study analyses the requirement of electricity with respect to the future population for the major forms of energy in the Lucknow city in Uttar Pradesh state of India. A model consisting of significant key energy indicators have been used for the estimation. Model wherever required refined in the second stage to remove the effect of auto-correlation. The accuracy of the model has been checked using standard statistical techniques and validated against the past data by testing for ‘expost’ forecast accuracy.

  13. Forecast of transportation energy demand through the year 2010

    Energy Technology Data Exchange (ETDEWEB)

    Mintz, M.M.; Vyas, A.D.

    1991-04-01

    Since 1979, the Center for Transportation Research (CTR) at Argonne National Laboratory (ANL) has produced baseline projections of US transportation activity and energy demand. These projections and the methodologies used to compute them are documented in a series of reports and research papers. As the lastest in this series of projections, this report documents the assumptions, methodologies, and results of the most recent projection -- termed ANL-90N -- and compares those results with other forecasts from the current literature, as well as with the selection of earlier Argonne forecasts. This current forecast may be used as a baseline against which to analyze trends and evaluate existing and proposed energy conservation programs and as an illustration of how the Transportation Energy and Emission Modeling System (TEEMS) works. (TEEMS links disaggregate models to produce an aggregate forecast of transportation activity, energy use, and emissions). This report and the projections it contains were developed for the US Department of Energy's Office of Transportation Technologies (OTT). The projections are not completely comprehensive. Time and modeling effort have been focused on the major energy consumers -- automobiles, trucks, commercial aircraft, rail and waterborne freight carriers, and pipelines. Because buses, rail passengers services, and general aviation consume relatively little energy, they are projected in the aggregate, as other'' modes, and used primarily as scaling factors. These projections are also limited to direct energy consumption. Projections of indirect energy consumption, such as energy consumed in vehicle and equipment manufacturing, infrastructure, fuel refining, etc., were judged outside the scope of this effort. The document is organized into two complementary sections -- one discussing passenger transportation modes, and the other discussing freight transportation modes. 99 refs., 10 figs., 43 tabs.

  14. NAVO NCOM Relocatable Model: Fukushima Regional Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary NCOM Relocatable 1km forecast model for Fukushima Region. USERS ARE REMINDED TO USE THE FUKUSHIMA 1KM NCOM DATA WITH CAUTION. THE MODEL WAS INITIATED ON...

  15. 39 CFR 3050.26 - Documentation of demand elasticities and volume forecasts.

    Science.gov (United States)

    2010-07-01

    ... Postal Service shall provide econometric estimates of demand elasticity for all postal products accompanied by the underlying econometric models and the input data sets used; and a volume forecast for the current fiscal year, and the underlying volume forecasting model....

  16. A fully adaptive forecasting model for short-term drinking water demand

    NARCIS (Netherlands)

    Bakker, M.; Vreeburg, J.H.G.; Schagen, van K.M.; Rietveld, L.C.

    2013-01-01

    For the optimal control of a water supply system, a short-term water demand forecast is necessary. We developed a model that forecasts the water demand for the next 48 h with 15-min time steps. The model uses measured water demands and static calendar data as single input. Based on this input, the m

  17. Future demand for electricity in the Nassau--Suffolk region

    Energy Technology Data Exchange (ETDEWEB)

    Carroll, T.W.; Palmedo, P.F.; Stern, R.

    1977-12-01

    Brookhaven National Laboratory established a new technology for load forecasting for the Long Island Lighting Company and prepared an independent forecast of the demand for electricity in the LILCO area. The method includes: demand for electricity placed in a total energy perspective so that substitutions between electricity and other fuels can be examined; assessment of the impact of conservation, new technology, gas curtailment, and other factors upon demand for electricity; and construction of the probability distribution of the demand for electricity. A detailed analysis of changing levels of demand for electricity, and other fuels, associated with these new developments is founded upon a disaggregated end-use characterization of energy utilization, including space heat, lighting, process energy, etc., coupled to basic driving forces for future demand, namely: population, housing mix, and economic growth in the region. The range of future events covers conservation, heat pumps, solar systems, storage resistance heaters, electric vehicles, extension of electrified rail, total energy systems, and gas curtailment. Based upon cost and other elements of the competition between technologies, BNL assessed the likelihood of these future developments. An optimistic view toward conservation leads to ''low'' demand for electricity, whereas rapid development of new technologies suggests ''high'' demand. (MCW)

  18. Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia.

    Directory of Open Access Journals (Sweden)

    Michael P Strager

    Full Text Available Predicting the locations of future surface coal mining in Appalachia is challenging for a number of reasons. Economic and regulatory factors impact the coal mining industry and forecasts of future coal production do not specifically predict changes in location of future coal production. With the potential environmental impacts from surface coal mining, prediction of the location of future activity would be valuable to decision makers. The goal of this study was to provide a method for predicting future surface coal mining extents under changing economic and regulatory forecasts through the year 2035. This was accomplished by integrating a spatial model with production demand forecasts to predict (1 km2 gridded cell size land cover change. Combining these two inputs was possible with a ratio which linked coal extraction quantities to a unit area extent. The result was a spatial distribution of probabilities allocated over forecasted demand for the Appalachian region including northern, central, southern, and eastern Illinois coal regions. The results can be used to better plan for land use alterations and potential cumulative impacts.

  19. Development of a neural network mathematical model for demand forecasting in fluctuating markets

    OpenAIRE

    Ziarati, Martin; Bilgili, Erdem; Singh, Lakhvir; Akdemir, Basak; Reza ZIARATI

    2013-01-01

    Research has shown that Neural Networks (NNs) when trained appropriately are the best forecasting system compared to conventional techniques. Research has shown that there is no system to accurately forecast sudden changes in demand for a given product. This paper reports on the development of a recovery method when a sudden change in demand has taken place. This error in forecasting demand leads to either excessive inventories of the product or shortages of it and can lead to substantial f...

  20. 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

  1. Operational, regional-scale, chemical weather forecasting models in Europe

    NARCIS (Netherlands)

    Kukkonen, J.; Balk, 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.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.

    2011-01-01

    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

  2. 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.

  3. Spare part demand forecasting for consumer goods using installed base information

    NARCIS (Netherlands)

    T.Y. Kim (Thai Young); R. Dekker (Rommert); C. Heij (Christiaan)

    2016-01-01

    textabstractWhen stopping production, the manufacturer has to decide on the lot size in the final production run to cover spare part demand during the end-of-life phase. This decision can be supported by forecasting how much demand is expected in the future. Forecasts can be obtained from the

  4. 76 FR 33398 - 60-Day Notice of Proposed Information Collection; Passport Demand Forecasting Study Phase III...

    Science.gov (United States)

    2011-06-08

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF STATE 60-Day Notice of Proposed Information Collection; Passport Demand Forecasting Study Phase III, 1405-0177 ACTION... Collection: Passport Demand Forecasting Study Phase III. OMB Control Number: OMB No. 1405-0177. Type of...

  5. 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...

  6. 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...

  7. Structural Analysis and Total Coal Demand Forecast in China

    Directory of Open Access Journals (Sweden)

    Qing Zhu

    2014-01-01

    Full Text Available Considering the speedy growth of industrialization and urbanization in China and the continued rise of coal consumption, this paper identifies factors that have impacted coal consumption in 1985–2011. After extracting the core factors, the Bayesian vector autoregressive forecast model is constructed, with variables that include coal consumption, the gross value of industrial output, and the downstream industry output (cement, crude steel, and thermal power. The impulse response function and variance decomposition are applied to portray the dynamic correlations between coal consumption and economic variables. Then for analyzing structural changes of coal consumption, the exponential smoothing model is also established, based on division of seven sectors. The results show that the structure of coal consumption underwent significant changes during the past 30 years. Consumption of both household sector and transport, storage, and post sectors continues to decline; consumption of wholesale and retail trade and hotels and catering services sectors presents a fluctuating and improving trend; and consumption of industry sector is still high. The gross value of industrial output and the downstream industry output have been promoting coal consumption growth for a long time. In 2015 and 2020, total coal demand is expected to reach 2746.27 and 4041.68 million tons of standard coal in China.

  8. 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.

  9. System Dynamics Approach to Urban Water Demand Forecasting A Case Study of Tianjin

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hongwei; ZHANG Xuehua; ZHANG Baoan

    2009-01-01

    A system dynamics approach to urban water demand forecasting was developed based on the analysis of urban water resources system.which was characterized by multi.feedback and nonlinear interactions among system elements.As an example,Tianjin water resources system dynamic model was set up to forecast water resources demand of the planning years.The practical verification showed that the relative error was lower than 1O%.Furthermore,through the comparison and analysis of the simulation results under different development modes presented in this paper.the forecasting results ofthe water resources demand ofTianiin was achieved based on sustainable utilization strategy of water resources.

  10. 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.

  11. 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.

  12. A Study of Demand Forecasting in the Defense Logistics Agency

    Science.gov (United States)

    1986-02-01

    methods employed backcasting in order to determine initial values for the key terms in the equations. Backcasting , a technique introduced by Box and...Management Simulation (USIMS) is a simulation model which can be used to examine the impacts of alternative inventory policies (in this case forecast...forecasts. Individual values were obtained for the smoothing term, alpha, and these *values were used in the comparisons. The backcasting technique * was

  13. 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.

  14. 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.

  15. 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

  16. System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts.

    Science.gov (United States)

    Qi, Cheng; Chang, Ni-Bin

    2011-06-01

    Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments.

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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.

  2. Modeling and Forecasting the Demand for Aircraft Recoverable Spare Parts

    Science.gov (United States)

    1993-01-01

    Squared Error and Mean Absolute Deviation of Improved Techniques over Current System ............................ vii S.2. Cost and Performance with... Current System .......... 77 7.2. Cost and Performance with Traditional Availability Goals ....................................... 80 7.3. Cost and...be im- proved by accouting explicitly for forecasting uncertainty, which the current method ignores. Our comments about the current system’s approach

  3. SARX Model Application for Industrial Power Demand Forecasting in Brazil

    Directory of Open Access Journals (Sweden)

    Alessandra de Ávila Montini

    2012-06-01

    Full Text Available The objective of this paper is to propose the application of the SARX model to arrive at industrial power consumption forecasts in Brazil, which are critical to support decision-making in the energy sector, based on technical, economic and environmentally sustainable grounds. The proposed model has a seasonal component and considers the influence of exogenous variables on the projection of the dependent variable and utilizes an autoregressive process for residual modeling so as to improve its explanatory power. Five exogenous variables were included: industrial capacity utilization, industrial electricity tariff, industrial real revenues, exchange rate, and machinery and equipment inflation. In addition, the model assumed that power forecast was dependent on its own time lags and also on a dummy variable to reflect 2009 economic crisis. The study used 84 monthly observations, from January 2003 to December 2009. The backward method was used to select exogenous variables, assuming a 0.10 descriptive value. The results showed an adjusted coefficient of determination of 93.9% and all the estimated coefficients were statistically significant at a 0.10 descriptive level. Forecasts were also made from January to May 2010 at a 95% confidence interval, which included actual consumption values for this period. The SARX model has demonstrated an excellent performance for industrial power consumption forecasting in Brazil.

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

    OpenAIRE

    Saravanan, S; Kannan, S.; C. Thangaraj

    2012-01-01

    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...

  5. 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.

  6. 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.

  7. 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.

  8. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    Energy Technology Data Exchange (ETDEWEB)

    Omitaomu, Olufemi A [ORNL; Li, Xueping [University of Tennessee, Knoxville (UTK); Zhou, Shengchao [University of Tennessee, Knoxville (UTK)

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

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

    Science.gov (United States)

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

    2015-12-01

    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.

  10. Forecasting Peak Load Electricity Demand Using Statistics and Rule Based Approach

    Directory of Open Access Journals (Sweden)

    Z. Ismail

    2009-01-01

    Full Text Available Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as the consequences of under or over forecasting the demand are serious and will affect all stakeholders in the electricity supply industry. Approach: If under estimated, the result is serious since plant installation cannot easily be advanced, this will affect the economy, business, loss of time and image. If over estimated, the financial penalty for excess capacity (i.e., over-estimated and wasting of resources. Therefore this study aimed to develop new forecasting model for forecasting electricity load demand which will minimize the error of forecasting. In this study, we explored the development of rule-based method for forecasting electricity peak load demand. The rule-based system synergized human reasoning style of fuzzy systems through the use of set of rules consisting of IF-THEN approximators with the learning and connectionist structure. Prior to the implementation of rule-based models, SARIMAT model and Regression time series were used. Results: Modification of the basic regression model and modeled it using Box-Jenkins auto regressive error had produced a satisfactory and adequate model with 2.41% forecasting error. With rule-based based forecasting, one can apply forecaster expertise and domain knowledge that is appropriate to the conditions of time series. Conclusion: This study showed a significant improvement in forecast accuracy when compared with the traditional time series model. Good domain knowledge of the experts had contributed to the increase in forecast accuracy. In general, the improvement will depend on the conditions of the data

  11. 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...... underestimation of travel demand observed in previous studies of build alternatives. These results indicate that the problem of systematic biases in travel demand forecasts for road projects have more problematic implications than previously assumed. The effect inflates rather than diminishes the problem...... of previously observed discrepancies between expected and observed travel demand, and the true magnitude of forecasting bias is thus greater than reported so far. The main implication for planning practice is that impact appraisals of road construction as a means of congestion relief appear overly beneficial...

  12. Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2017-03-01

    Full Text Available Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA and Cuckoo Optimization Algorithm (COA, are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.

  13. Operational Planning of Channel Airlift Missions Using Forecasted Demand

    Science.gov (United States)

    2013-03-01

    that are equivalent to the forecasted cargo amount. The simulated pallets are then used in a heuristic cargo loading algorithm. The loading...carriers. Participating carriers must make their aircraft and aircrews available on short notice, with some of the suddenness mitigated through two...major crisis, including aircrews , and maintenance, at that time, would have been anywhere from $15 to $50 billion. It could be argued that the DOD

  14. A STUDY ON NEW PRODUCT DEMAND FORECASTING BASED ON BASS DIFFUSION MODEL

    Directory of Open Access Journals (Sweden)

    Zuhaimy Ismail

    2013-01-01

    Full Text Available A forecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. This study considers the Bass Model for forecasting the diffusion of new products or an innovation in the Malaysian society. The objective of the proposed model is to represent the level of spread on new products among a given set of society in terms of a simple mathematical function that elapsed since the introduction of new products. With limited amount of data available for new products, a robust Bass model was developed to forecast the sales volume. A procedure of the proposed diffusion model was designed and the parameters were estimated. Results obtained by applying the proposed model and numerical calculation show that the proposed Bass diffusion model is robust and effective for forecasting demand of new products. This study concludes that the newly developed bass diffusion of demand function has significantly contributed for forecasting the diffusion of new products.

  15. 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

  16. 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.

  17. Electric Energy Demand Forecast of Nanchang based on Cellular Genetic Algorithm and BP Neural Network

    OpenAIRE

    Cheng Yugui

    2013-01-01

    A kind of power forecast model combined cellular genetic algorithm with BP neural network was established in this article. Mid-long term power demand in urban areas was done load forecasting and analysis based on material object of the actual power consumption in urban areas of Nanchang. The results show that this method has the characteristic of the minimum training times, the shortest consumption time, the minimum error and the shortest operation time to obtain the best fitting effect.  

  18. 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...

  19. Forecasting low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Noor Yasmin Zainun

    2011-07-01

    Full Text Available Low cost housing is one of the government main agenda in fulfilling nation’s housing need. Thus, it is very crucial to forecast the housing demand because of economic implication to national interest. Neural Networks (ANN is one of the tools that can predict the demand. This paper presents a work on developing   a model to forecast low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks approach. The actual and forecasted data are compared and validate using Mean Absolute Percentage Error (MAPE. It was found that the best NN model to forecast low-cost housing in state of Pahang is 1-22-1 with 0.7 learning rate and 0.4 momentum rate. The MAPE value for the comparison between the actual and forecasted data is 2.63%. This model is helpful to the related agencies such as developer or any other relevant government agencies in making their development planning for low cost housing demand in Pahang.

  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

    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. 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

  2. Forecasting irrigation demand by assimilating satellite images and numerical weather predictions

    Science.gov (United States)

    Pelosi, Anna; Medina, Hanoi; Villani, Paolo; Falanga Bolognesi, Salvatore; D'Urso, Guido; Battista Chirico, Giovanni

    2016-04-01

    Forecasting irrigation water demand, with small predictive uncertainty in the short-medium term, is fundamental for an efficient planning of water resource allocation among multiple users and for decreasing water and energy consumptions. In this study we present an innovative system for forecasting irrigation water demand, applicable at different spatial scales: from the farm level to the irrigation district level. The forecast system is centred on a crop growth model assimilating data from satellite images and numerical weather forecasts, according to a stochastic ensemble-based approach. Different sources of uncertainty affecting model predictions are represented by an ensemble of model trajectories, each generated by a possible realization of the model components (model parameters, input weather data and model state variables). The crop growth model is based on a set of simplified analytical relations, with the aim to assess biomass, leaf area index (LAI) growth and evapotranspiration rate with a daily time step. Within the crop growth model, LAI dynamics is let be governed by temperature and leaf dry matter supply, according to the development stage of the crop. The model assimilates LAI data retrieved from VIS-NIR high-resolution multispectral satellite images. Numerical weather model outputs are those from the European limited area ensemble prediction system (COSMO-LEPS), which provides forecasts up to five days with a spatial resolution of seven kilometres. Weather forecasts are sequentially bias corrected based on data from ground weather stations. The forecasting system is evaluated in experimental areas of southern Italy during three irrigation seasons. The performance analysis shows very accurate irrigation water demand forecasts, which make the proposed system a valuable support for water planning and saving at farm level as well as for water management at larger spatial scales.

  3. Short-Term Electrical Peak Demand Forecasting in a Large Government Building Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Jason Grant

    2014-03-01

    Full Text Available The power output capacity of a local electrical utility is dictated by its customers’ cumulative peak-demand electrical consumption. Most electrical utilities in the United States maintain peak-power generation capacity by charging for end-use peak electrical demand; thirty to seventy percent of an electric utility’s bill. To reduce peak demand, a real-time energy monitoring system was designed, developed, and implemented for a large government building. Data logging, combined with an application of artificial neural networks (ANNs, provides short-term electrical load forecasting data for controlled peak demand. The ANN model was tested against other forecasting methods including simple moving average (SMA, linear regression, and multivariate adaptive regression splines (MARSplines and was effective at forecasting peak building electrical demand in a large government building sixty minutes into the future. The ANN model presented here outperformed the other forecasting methods tested with a mean absolute percentage error (MAPE of 3.9% as compared to the SMA, linear regression, and MARSplines MAPEs of 7.7%, 17.3%, and 7.0% respectively. Additionally, the ANN model realized an absolute maximum error (AME of 8.2% as compared to the SMA, linear regression, and MARSplines AMEs of 26.2%, 45.1%, and 22.5% respectively.

  4. 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.

  5. 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.

  6. The Barcelona Dust Forecast Center: The first WMO regional meteorological center specialized on atmospheric sand and dust forecast

    Science.gov (United States)

    Basart, Sara; Terradellas, Enric; Cuevas, Emilio; Jorba, Oriol; Benincasa, Francesco; Baldasano, Jose M.

    2015-04-01

    The World Meteorological Organization's Sand and Dust Storm Warning Advisory and Assessment System (WMO SDS-WAS, http://sds-was.aemet.es/) project has the mission to enhance the ability of countries to deliver timely and quality sand and dust storm forecasts, observations, information and knowledge to users through an international partnership of research and operational communities. The good results obtained by the SDS-WAS Northern Africa, Middle East and Europe (NAMEE) Regional Center and the demand of many national meteorological services led to the deployment of operational dust forecast services. On June 2014, the first WMO Regional Meteorological Center Specialized on Atmospheric Sand and Dust Forecast, the Barcelona Dust Forecast Center (BDFC; http://dust.aemet.es/), was publicly presented. The Center operationally generates and distributes predictions for the NAMEE region. The dust forecasts are based on the NMMB/BSC-Dust model developed at the Barcelona Supercomputing Center (BSC-CNS). The present contribution will describe the main objectives and capabilities of BDFC. One of the activities performed by the BDFC is to establish a protocol to routinely exchange products from dust forecast models as dust load, dust optical depth (AOD), surface concentration, surface extinction and deposition. An important step in dust forecasting is the evaluation of the results that have been generated. This process consists of the comparison of the model results with multiple kinds of observations (i.e. AERONET and MODIS) and is aimed to facilitate the understanding of the model capabilities, limitations, and appropriateness for the purpose for which it was designed. The aim of this work is to present different evaluation approaches and to test the use of different observational products in the evaluation system.

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

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Hang T.; Nabney, Ian T. [Non-linearity and Complexity Research Group, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET (United Kingdom)

    2010-09-15

    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)

  8. 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.

  9. 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.

  10. Towards more behaviourally robust travel demand forecasts: Catering to utility maximisers and regret minimisers

    NARCIS (Netherlands)

    Dekker, T.; Van Cranenburgh, S.; Chorus, C.G.

    2013-01-01

    Choice probabilities and related outputs of discrete choice models form a critical input to many travel demand forecasting and transport project evaluation studies. The decision rule underlying a discrete choice model describes how individuals make their decisions and thereby co-determines the

  11. 76 FR 53704 - 30-Day Notice of Proposed Information Collection: Passport Demand Forecasting Study Phase III...

    Science.gov (United States)

    2011-08-29

    ... the Office of Management and Budget (OMB) for approval in accordance with the Paperwork Reduction Act of 1995. Title of Information Collection: Passport Demand Forecasting Study Phase III. OMB Control.... Frequency: Monthly. Obligation to Respond: Voluntary. DATES: Submit comments to the Office of Management...

  12. 基于主成分回归的区域物流需求预测研究--以云南省为例%Regional Logistics Demand Forecasting Based on Principal Component Regression:Taking Yunnan Province as an Example

    Institute of Scientific and Technical Information of China (English)

    彭湖; 何民

    2015-01-01

    In order to provide the quantitative scale data of the regional logistics demand for making the regional logistics development policies, determining the logistics infrastructure construction scale, ana⁃lyzing logistics market situation and so on, the establishment of scientific and reasonable prediction mod⁃el is particularly important. Firstly, the relationship between regional logistics and regional economy was studied. Secondly, the freight turnover quantity was selected from the freight quantity and the freight turnover quantity to characterize the regional logistics demand scale. Finally, primary industry value, secondary industry value, tertiary industry value, total retail sales of consumer goods,fixed asset invest⁃ment volume, value of export and import etc., were selected from regional economy indexes to be as influ⁃encing factors. Based on statistical data of Yunnan Province, using SPSS statistical analysis software, the region logistics demand forecasting model based on principal component regression method was estab⁃lished. The research confirmed that the model for forecasting the logistics demand scale of Yunnan Prov⁃ ince, the average relative error of the model is less than 4%, the model has higher prediction accuracy and can be used as medium and short term logistics demand forecasting tool.%为了能够给区域物流发展政策的制定、物流基础设施建设规模的确定、物流市场态势的分析等提供定量的物流需求规模数据,建立科学合理的预测模型显得尤为重要。首先,研究区域物流与区域经济的关系;其次,从货运量、货运周转量两个指标中选取货运周转量来表征区域物流需求规模;最后,从区域经济指标中选取第一产业总产值、第二产业总产值、第三产业总产值、社会消费品零售总额、固定资产投资额、进出口额等指标作为影响因素,借助SPSS统计分析软件,以云南省统计数据为基础

  13. 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.

  14. 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

  15. 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

  16. 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)

  17. Seasonal Verification of Dust Forecast over the Indian Region

    Science.gov (United States)

    Fatima, Hashmi; George, John P.; Rajagopal, E. N.; Basu, Swati

    2017-07-01

    The medium-range forecast of the dust aerosols over Indian region produced by the NCMRWF numerical weather prediction model with mineral dust scheme from May 2013 to May 2014 is examined in this study. Coarse mode aerosol observations are only used for comparison with dust forecast with the assumption that coarse mode aerosol over Indian region largely represents dust aerosol, especially over the areas of high dust load. Accuracy and trends of the day-to-day dust forecast are studied at three AERONET locations in Indo-Gangetic Plains (IGP) using surface and MODIS satellite retrievals of coarse mode aerosol optical depth for entire one year (May 2013-May 2014). Seasonal mean geographical distribution of the medium-range forecast of dust by the model over Indian region is validated with different satellite retrievals for all four seasons. Availability of suitable observations is one of the limiting factors and big challenges for the validation of the dust forecast. The main focus of this study is to assess dust forecast by the model over Indian region for all seasons, to know the biases and errors of the model forecast for its optimal use. The study finds that model dust forecast is comparable to AERONET observations over three locations for all seasons except monsoon season.

  18. Electric Energy Demand Forecast of Nanchang based on Cellular Genetic Algorithm and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Cheng Yugui

    2013-07-01

    Full Text Available A kind of power forecast model combined cellular genetic algorithm with BP neural network was established in this article. Mid-long term power demand in urban areas was done load forecasting and analysis based on material object of the actual power consumption in urban areas of Nanchang. The results show that this method has the characteristic of the minimum training times, the shortest consumption time, the minimum error and the shortest operation time to obtain the best fitting effect.  

  19. 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

  20. Supporting Urban Planning of Low-Carbon Precincts: Integrated Demand Forecasting

    Directory of Open Access Journals (Sweden)

    Steffen Lehmann

    2013-12-01

    Full Text Available Waste is a symbol of inefficiency in modern society and represents misallocated resources. This paper outlines an on-going interdisciplinary research project entitled “Integrated ETWW demand forecasting and scenario planning for low-carbon precincts” and reports on first findings and a literature review. This large multi-stakeholder research project develops a shared platform for integrated ETWW (energy, transport, waste and water planning in a low-carbon urban future, focusing on synergies and alternative approaches to urban planning. The aim of the project is to develop a holistic integrated software tool for demand forecasting and scenario evaluation for residential precincts, covering the four domains, ETWW, using identified commonalities in data requirements and model formulation. The authors of this paper are overseeing the waste domain. A major component of the project will be developing a method for including the impacts of household behavior change in demand forecasting, as well as assessing the overall carbon impacts of urban developments or redevelopments of existing precincts. The resulting tool will allow urban planners, municipalities and developers to assess the future total demands for energy, transport, waste and water whilst in the planning phase. The tool will also help to assess waste management performance and materials flow in relation to energy and water consumption and travel behavior, supporting the design and management of urban systems in different city contexts.

  1. Analysis and Modeling for China’s Electricity Demand Forecasting Based on a New Mathematical Hybrid Method

    Directory of Open Access Journals (Sweden)

    Jie Liang

    2017-03-01

    Full Text Available Electricity demand forecasting can provide the scientific basis for the country to formulate the power industry development strategy and the power-generating target, which further promotes the sustainable, healthy and rapid development of the national economy. In this paper, a new mathematical hybrid method is proposed to forecast electricity demand. In line with electricity demand feature, the framework of joint-forecasting model is established and divided into two procedures: firstly, the modified GM(1,1 model and the Logistic model are used to make single forecasting. Then, the induced ordered weighted harmonic averaging operator (IOWHA is applied to combine these two single models and make joint-forecasting. Forecasting results demonstrate that this new hybrid model is superior to both single-forecasting approaches and traditional joint-forecasting methods, thus verifying the high prediction validity and accuracy of mentioned joint-forecasting model. Finally, detailed forecasting-outcomes on electricity demand of China in 2016–2020 are discussed and displayed a slow-growth smoothly over the next five years.

  2. Forecasting Drinking and Household Water Requirement of the Thrace Region

    Directory of Open Access Journals (Sweden)

    F. Konukcu

    2007-05-01

    Full Text Available This study aims at future forecasting drinking and household water requirements of the Thrace region by the aid of a scientific perspective. To realise this, first future population of the region was predicted and then the water requirements were calculated. As results, water requirements of the city and the countryside for the years 2020, 2030, 2040 and 2050 were computed as 1.45, 1.94, 2.58 and 3.44 km3, respectively. Beside, rapidly increasing drinking and household water requirements due to fast population growth and immense amount of migration into the region, demands by agriculture and intensive industry suggest that the present total water potential of about 4.0 km3 will not be sufficient and a great water crisis may be experienced. Adverse effects of a probable global climate change on water resources make the situation more acute. To overcome this crisis, governmental agencies and civil societies are called work together to produce and implement rational strategies.

  3. 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.

  4. 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...

  5. 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 and overesti......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...... and overestimating the demand for transport infrastructure projects. It is therefore claimed that ex-anteevaluations of trans- port-related projects are often based on inaccurate material, which ultimately can lead to severe socio- economic misperformance. This paper seeks to bridge the gap between the inaccuracies...... 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...

  6. 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

  7. Forecasting electricity demand in South Africa: A critique of Eskom’s projections

    Directory of Open Access Journals (Sweden)

    Anastassios Pouris

    2010-03-01

    Full Text Available Within a short period, Eskom has applied to the National Energy Regulator of South Africa (NERSA for the third time since the 2008 electricity crisis, proposing a multiyear price determination for the periods 2010−2011 and 2012−2013. The new application, submitted at the end of September 2009, motivated for the debate of strategies with which the consequences of the proposed price hikes could be predicted, measured and controlled. In his presentation to Parliament in February 2009, Eskom’s then CEO, Mr Jacob Maroga presented the current energy situation in the country, the reasons for the crisis in 2007−2008, as well as the challenges of the future. The purpose of this paper is to contribute some new ideas and perspectives to Eskom’s existing arguments regarding the demand for electricity. The most important issue is the fact that Eskom does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. This study proposed that prices have a high impact on the demand for electricity (price elasticity of -0.5. Employing similar assumptions for the country’s economic growth as Eskom, the results of the forecasting exercise indicated a substantial decrease in demand (scenario 1: -31% in 2025 and scenario 2:-18% in 2025. This study’s findings contrasted significantly with Eskom’s projection, which has extensive implications as far as policy is concerned.

  8. 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. 

  9. 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.

  10. Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

    Science.gov (United States)

    Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

    2016-10-01

    Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

  11. Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

    Science.gov (United States)

    Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

    2017-08-01

    Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

  12. 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

  13. 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

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

    Science.gov (United States)

    Shukla, S.; McNally, A.; Husak, G.; Funk, C.

    2014-03-01

    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 is

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

    Directory of Open Access Journals (Sweden)

    S. Shukla

    2014-03-01

    Full Text Available 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

  16. Using Adjoint-Based Forecast Sensitivity Method to Evaluate TAMDAR Data Impacts on Regional Forecasts

    Directory of Open Access Journals (Sweden)

    Xiaoyan Zhang

    2015-01-01

    Full Text Available This study evaluates the impact of Tropospheric Airborne Meteorological Data Reporting (TAMDAR observations on regional 24-hour forecast error reduction over the Continental United States (CONUS domain using adjoint-based forecast sensitivity to observation (FSO method as the diagnostic tool. The relative impact of TAMDAR observations on reducing the forecast error was assessed by conducting the WRFDA FSO experiments for two two-week-long periods, one in January and one in June 2010. These experiments assimilated operational TAMDAR data and other conventional observations, as well as GPS refractivity (GPSREF. FSO results show that rawinsonde soundings (SOUND and TAMDAR exhibit the largest observation impact on 24 h WRF forecast, followed by GeoAMV, aviation routine weather reports (METAR, GPSREF, and synoptic observations (SYNOP. At 0000 and 1200 UTC, TAMDAR has an equivalent impact to SOUND in reducing the 24-hour forecast error. However, at 1800 UTC, TAMDAR has a distinct advantage over SOUND, which has the sparse observation report at these times. In addition, TAMDAR humidity observations at lower levels of the atmosphere (700 and 850 hPa have a significant impact on 24 h forecast error reductions. TAMDAR and SOUND observations present a qualitatively similar observation impact between FSO and Observation System Experiments (OSEs.

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

    NARCIS (Netherlands)

    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, K.

    2012-01-01

    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

  18. 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.

  19. Forecasting of Sporadic Demand Patterns with Seasonality and Trend Components: An Empirical Comparison between Holt-Winters and (SARIMA Methods

    Directory of Open Access Journals (Sweden)

    Rita Gamberini

    2010-01-01

    Full Text Available Items with irregular and sporadic demand profiles are frequently tackled by companies, given the necessity of proposing wider and wider mix, along with characteristics of specific market fields (i.e., when spare parts are manufactured and sold. Furthermore, a new company entering into the market is featured by irregular customers' orders. Hence, consistent efforts are spent with the aim of correctly forecasting and managing irregular and sporadic products demand. In this paper, the problem of correctly forecasting customers' orders is analyzed by empirically comparing existing forecasting techniques. The case of items with irregular demand profiles, coupled with seasonality and trend components, is investigated. Specifically, forecasting methods (i.e., Holt-Winters approach and (SARIMA available for items with seasonality and trend components are empirically analyzed and tested in the case of data coming from the industrial field and characterized by intermittence. Hence, in the conclusions section, well-performing approaches are addressed.

  20. 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,...

  1. Regional Supply and Demand for Library Services.

    Science.gov (United States)

    Foust, James D.; Hughes, Warren R.

    This study contains an inventory of Indiana's present library facilities together with projections of the need for future library resources based on the population projections. To facilitate presentation and analysis of the data in this report, 14 state planning regions were used. The relevant geographic regions section defines Indiana's economic…

  2. Nearest neighbour models for local and regional avalanche forecasting

    Directory of Open Access Journals (Sweden)

    M. Gassner

    2002-01-01

    Full Text Available This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Re-search (SLF. Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.

  3. Artificial Neural Networks and Support Vector Machines for Water Demand Time Series Forecasting

    CERN Document Server

    Msiza, Ishmael S; Nelwamondo, Fulufhelo Vincent

    2007-01-01

    Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life. Water supply entities therefore have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modelling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are the Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs). In this study it was observed that the ANNs perform better than the SVMs. This performance is measured against the generalisation ability of the two.

  4. Regional demands for policy participation in the EU multilevel system

    Directory of Open Access Journals (Sweden)

    Philipp Studinger

    2012-06-01

    Full Text Available Over the past 50 years, an increasing amount of political authority has been delegated to the regional government level in Europe. This paper analyses regional demands for involvement in policy-making by focusing on the preferences of top-level regional civil servants (“regio-crats”. A survey (n=347 of regio-crats in 60 regions of 5 European Union member states serves as the empirical basis for the analysis of regional demands for policy involvement in the multilevel system. The data reveal differential patterns of demands. By and large, regio-crats emerge as being conservative, incremental and modest in their wishes for greater policy involvement, except where the regional contexts are characterised by substantial emancipatory political ambitions or cultural distinctiveness. Regional demands for policy participation in the multilevel system are pragmatic, patch-worked and incremental, and more conservative than transformative.

  5. 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. 

  6. 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.

  7. Forecasts of Mobile Broadband Development in the Asia-Pacific Region

    Institute of Scientific and Technical Information of China (English)

    2012-01-01

    The mobile broadband developed dramatically. Asia Pacific is one of the most important forces in the whole world. Mobile cellular subscriptions in the Asia-Pacific region have increased 22% from 2009 to 2010, much higher than other regions. A compound annual growth rate of data services is almost 100% in the past several years. This paper forecasts the demands of the mobile broadband development and estimates the needs for spectrum resources for next decade in the Asia-Pacific region. In order to make IMT create greater value for the human beings, sufficient spectrum resources are highly necessary.

  8. 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.

  9. Development of demand forecasting tool for natural resources recouping from municipal solid waste.

    Science.gov (United States)

    Zaman, Atiq Uz; Lehmann, Steffen

    2013-10-01

    Sustainable waste management requires an integrated planning and design strategy for reliable forecasting of waste generation, collection, recycling, treatment and disposal for the successful development of future residential precincts. The success of the future development and management of waste relies to a high extent on the accuracy of the prediction and on a comprehensive understanding of the overall waste management systems. This study defies the traditional concepts of waste, in which waste was considered as the last phase of production and services, by putting forward the new concept of waste as an intermediate phase of production and services. The study aims to develop a demand forecasting tool called 'zero waste index' (ZWI) for measuring the natural resources recouped from municipal solid waste. The ZWI (ZWI demand forecasting tool) quantifies the amount of virgin materials recovered from solid waste and subsequently reduces extraction of natural resources. In addition, the tool estimates the potential amount of energy, water and emissions avoided or saved by the improved waste management system. The ZWI is tested in a case study of waste management systems in two developed cities: Adelaide (Australia) and Stockholm (Sweden). The ZWI of waste management systems in Adelaide and Stockholm is 0.33 and 0.17 respectively. The study also enumerates per capita energy savings of 2.9 GJ and 2.83 GJ, greenhouse gas emissions reductions of 0.39 tonnes (CO2e) and 0.33 tonnes (CO2e), as well as water savings of 2.8 kL and 0.92 kL in Adelaide and Stockholm respectively.

  10. Forecasting for materials with intermittent demand based on combined forecasting%基于组合预测的间断性需求器材预测

    Institute of Scientific and Technical Information of China (English)

    许绍杰; 张衡; 聂涛; 王晗中

    2012-01-01

    为了提高间断性需求装备器材的预测精度,提出一种组合预测模型.该模型从解释变量序列和自相关序列两个方面进行组合预测,对解释变量序列采用Logistic回归模型预测提前期非零需求发生概率,对自相关序列采用Markov过程估计提前期非零需求发生概率,整合两个预测结果得到最终的提前期需求.实验结果表明,该预测模型具有较高的预测精度.%In order to enhance the forecasting accuracy of materials with intermittent demand, a combined forecasting model is proposed. This model disintegrates the time series into explanatory series and auto-correlated series. Then the probabilities of nonzero demands for the two series in lead time are estimated by Logistic regression model and Markov forecasting respectively. The final demand forecasting result is calculated through integrating the results of the two series. Experiment results show that the forecasting model can get high precision.

  11. 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.

  12. 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.

  13. 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.

  14. Impact of High Resolution SST Data on Regional Weather Forecasts

    Science.gov (United States)

    Jedlovec, Gary J.; Case, Jonathon; LaFontaine, Frank; Vazquez, Jorge; Mattocks, Craig

    2010-01-01

    Past studies have shown that the use of coarse resolution SST products such as from the real-time global (RTG) SST analysis[1] or other coarse resolution once-a-day products do not properly portray the diurnal variability of fluxes of heat and moisture from the ocean that drive the formation of low level clouds and precipitation over the ocean. For example, the use of high resolution MODIS SST composite [2] to initialize the Advanced Research Weather Research and Forecast (WRF) (ARW) [3] has been shown to improve the prediction of sensible weather parameters in coastal regions [4][5}. In an extend study, [6] compared the MODIS SST composite product to the RTG SST analysis and evaluated forecast differences for a 6 month period from March through August 2007 over the Florida coastal regions. In a comparison to buoy data, they found that that the MODIS SST composites reduced the bias and standard deviation over that of the RTG data. These improvements led to significant changes in the initial and forecasted heat fluxes and the resulting surface temperature fields, wind patterns, and cloud distributions. They also showed that the MODIS composite SST product, produced for the Terra and Aqua satellite overpass times, captured a component of the diurnal cycle in SSTs not represented in the RTG or other one-a-day SST analyses. Failure to properly incorporate these effects in the WRF initialization cycle led to temperature biases in the resulting short term forecasts. The forecast impact was limited in some situations however, due to composite product inaccuracies brought about by data latency during periods of long-term cloud cover. This paper focuses on the forecast impact of an enhanced MODIS/AMSR-E composite SST product designed to reduce inaccuracies due data latency in the MODIS only composite product.

  15. 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.

  16. Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

    Science.gov (United States)

    Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

    2016-06-01

    Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

  17. Development of golf tourism and golf tourism demand forecasts in Turkey: a study of Belek region Türkiye’de golf turizminin gelişimi ve golf turizmi talebi tahminleri: Belek bölgesine yönelik bir çalışma

    Directory of Open Access Journals (Sweden)

    Murat Çuhadar

    2013-06-01

    Full Text Available Golf tourism has become one of the rapidly developing tourism types in Turkey, especially in the Belek region. In this study, detailed information about the development of golf tourism in Turkey from past to present was provided and  golf tourism demand to Belek region which is a major golf tourism destinastion in the world and Turkey was modeled and forecasted monthly by Box-Jenkins methodology for the May 2013 –December 2014 period. As a measure of golf tourism demand, number of monthly golf games were taken in the study and the monthly number of golf game statistics of January 2001 – April 2013 in the golf establishments in Belek tourism center were used. By producing ex-ante forecasts it is aimed to create a basis for tourism development plans prepared by the management of private and public sector and to provide support for administrators’ monthly planning decisions.Golf turizmi, Belek bölgesi başta olmak üzere Türkiye’de hızla gelişen turizm türlerinden biri haline gelmiştir. Bu çalışmada, Türkiye’de golf turizminin geçmişten günümüze gelişimi hakkında ayrıntılı bilgiler sunularak, Türkiye’nin ve dünyanın önde gelen golf turizmi merkezlerinden olan Belek turizm merkezine yönelik golf turizmi talebi Box-Jenkins metodoljisi ile modellenmiş ve 2013 (Mayıs itibariyle ve 2014 yılları için aylık olarak tahmin edilmiştir. Çalışmada golf turizmi talebinin ölçüsü olarak golf oyun sayıları alınmış ve Ocak 2001 – Nisan 2013 döneminde Belek turizm merkezindeki golf tesislerinde gerçekleşen aylık golf oyun sayısı istatistiklerinden yararlanılmıştır. Yapılan tahminler ile, özel sektör ve kamu yönetimleri tarafından hazırlanan turistik gelişme planları için bir zemin oluşturulması ve ilgili yöneticilerin aylık planlamalarında karar almalarına destek sağlanması amaçlanmıştır.

  18. FORECAST OF THE AGRICULTURAL DEVELOPMENT FOR THE AMUR REGION

    Directory of Open Access Journals (Sweden)

    Reymer V. V.

    2015-12-01

    Full Text Available This article explains the relevance of evaluation of agricultural growth, which can be achieved through the implementation of agricultural sectors’ innovative potential. The opportunities of agricultural growth are defined by the set of macroeconomic, sectoral and regional factors as well as the type of enterprises that have different levels of innovative susceptibility. The authors give an overview of the main methods of social and economic forecasting and justify the choice of the ARIMA (Autoregressive integrated moving average as a tool for forecasting regional development of agriculture. The article presents the experts’ estimatesbased values of integrated indicators of agricultural exogenous factors and the ARIMA-parameters based on the use of these indicators for time series prediction of agricultural production in the Amur region. The authors conclude that the time series ARIMA-model of the gross agricultural production, taking into account the influence of innovation potential factors, demonstrate a good approximation to the Amur region data. This article also compares the forecasts of agricultural production on inertial and innovative scenario for the Amur region, and provides an estimation of innovation potential growth of the agricultural branches

  19. 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.

  20. Operation of Battery Energy Storage System in Demand Side using Local Load Forecasting

    Science.gov (United States)

    Hida, Yusuke; Yokoyama, Ryuichi; Shimizukawa, Jun; Iba, Kenji; Tanaka, Kouji; Seki, Tomomichi

    Recently, the various political movements, which reduce CO2-emission, have been proposed against global warming. Therefore, battery energy storage systems (BESSs) such as NAS (sodium and sulfur) battery are attracting attention around the world. The first purpose of BESS was the improvement of load factors. The second purpose is the improvement of power quality, especially against voltage-sag. The recent interest is oriented to utilize BESS to mitigate the intermittency of renewable energy. NAS battery has two operation modes. The first one is a fixed pattern operation, which is time-schedule in advance. The second mode is the load following operation. Although this mode can perform more the flexible operation by adjusting the change of load, it has the risks of shortage/surplus of battery energy. In this paper, an accurate demand forecasting method, which is based on multiple regression models, is proposed. Using this load forecasting, the more advanced control of load following operation for NAS battery is proposed.

  1. Status of China's Refined Oil Products Consumption and Forecast on Demand During the"11th Five-Year Plan" Period

    Institute of Scientific and Technical Information of China (English)

    Bai Lu

    2007-01-01

    The author used two common methods in this industry.i.e.the"Consumption Coefficient Method"and the"Elasticity Coefficient Trend Method",to forecast the refined oil product demand in 2010.Through analyzing and comparing the two forecast results,it is projected that the demand for finished product oils in 2010 will be in the range of 220 to 240 million tons a year.In addition,out of concern about the total oil products consumption to exceed 600 million tons/year in 2020,the author puts forward suggestions and measures aimed at conservation of oil products and application of alternative fuels.

  2. Research Note—Prerelease Demand Forecasting for Motion Pictures Using Functional Shape Analysis of Virtual Stock Markets

    OpenAIRE

    Natasha Zhang Foutz; Wolfgang Jank

    2010-01-01

    Prerelease demand forecasting is one of the most crucial yet difficult tasks facing marketers in the $60 billion motion picture industry. We propose functional shape analysis (FSA) of virtual stock markets (VSMs) to address this long-standing challenge. In VSMs, prices of a movie's stock reflect the dynamic demand expectations prior to the movie's release. Using FSA, we identify a small number of distinguishing shapes, e.g., the last-moment velocity spurt, that carry information about a movie...

  3. Regional Differences in the Demand for Agricultural Socialized Service

    Institute of Scientific and Technical Information of China (English)

    Lu; LI

    2015-01-01

    With the gradual deepening of China’s agricultural modernization,establishing a sound agricultural socialized service system is of great significance to improving the efficiency of resource use and achieving sustainable agricultural development. On the basis of the micro survey data on China’s 12 provinces,we analyze the intensity of demand for agricultural socialized service in different regions as well as the main factors influencing farmers’ demand willingness. The results show that there are regional differences in farmers’ demand for agricultural socialized service,and among multiple factors affecting the demand willingness of agricultural socialized service,farmers’ individual characteristics,household economic characteristics and farmers’ social communication behavior have varying degrees of impact on farmers’ choice of service.

  4. Ionospheric forecasts for the European region for space weather applications

    Directory of Open Access Journals (Sweden)

    Tsagouri Ioanna

    2015-01-01

    Full Text Available This paper discusses recent advances in the implementation and validation of the Solar Wind driven autoregression model for Ionospheric short-term Forecast (SWIF that is running in the European Digital upper Atmosphere Server (DIAS to release ionospheric forecasting products for the European region. The upgraded implementation plan expands SWIF’s capabilities in the high latitude ionosphere while the extensive validation tests in the two solar cycles 23 and 24 allow the comprehensive analysis of the model’s performance in all terms. Focusing on disturbed conditions, the results demonstrate that SWIF’s alert detection algorithm forecasts the occurrence of ionospheric storm time disturbances with probability of detection up to 98% under intense geomagnetic storm conditions and up to 63% when storms of moderate intensity are also considered. The forecasts show relative improvement over climatology of about 30% in middle-to-low and high latitudes and 40% in middle-to-high latitudes. This indicates that SWIF is able to capture on average more than one third (35% of the storm-associated ionospheric disturbances. Regarding the accuracy, the averaged mean relative error during storm conditions usually ranges around 20% in middle-to-low and high latitudes and 24% in the middle-to-high latitudes. Our analysis shows clearly that SWIF alert criteria were designed to effectively anticipate the ionospheric storm time effects that occurred under specific interplanetary conditions, e.g., cloud Interplanetary Coronal Mass Ejections (ICMEs and/or associated sheaths. The results provide valuable input in advancing our ability in predicting the space weather effects in the ionosphere for future developments, and further work is proposed to enhance the model forecasting efficiency to support operational applications.

  5. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

    Science.gov (United States)

    Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

    2017-02-01

    Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

  6. 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 This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, the authors forecast house price inflation for five...

  7. 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,...

  8. 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)...

  9. Regional probabilistic fertility forecasting by modeling between-country correlations

    Directory of Open Access Journals (Sweden)

    Bailey Fosdick

    2014-04-01

    Full Text Available Background: The United Nations (UN Population Division constructs probabilistic projections for the total fertility rate (TFR using the Bayesian hierarchical model of Alkema et al. (2011, which produces predictive distributions of the TFR for individual countries. The UN is interested in publishing probabilistic projections for aggregates of countries, such as regions and trading blocs. This requires joint probabilistic projections of future countryspecific TFRs, taking account of the correlations between them. Objective: We propose an extension of the Bayesian hierarchical model that allows for probabilistic projection of aggregate TFR for any set of countries. Methods: We model the correlation between country forecast errors as a linear function of time invariant covariates, namely whether the countries are contiguous, whether they had a common colonizer after 1945, and whether they are in the same UN region. The resulting correlation model is incorporated into the Bayesian hierarchical model's error distribution. Results: We produce predictive distributions of TFR for 1990-2010 for each of the UN's primary regions. We find that the proportions of the observed values that fall within the prediction intervals from our method are closer to their nominal levels than those produced by the current model. Conclusions: Our results suggest that a substantial proportion of the correlation between forecast errors for TFR in different countries is due to the countries' geographic proximity to one another, and that if this correlation is accounted for, the quality of probabilistic projections of TFR for regions and other aggregates is improved.

  10. Regional studies program. Forecasting the local economic impacts of energy resource development: a methodological approach

    Energy Technology Data Exchange (ETDEWEB)

    Stenehjem, E.J.

    1975-12-01

    Emphasis is placed on the nature and magnitude of socio-economic impacts of fossil-fuel development. A model is described that identifies and estimates the magnitude of the economic impacts of anticipated energy resource development in site-specific areas and geographically contiguous areas of unspecified size. The modeling methodology was designed to assist industries and government agencies complying with recent federal and state legislation requiring subregional impact analyses for individual facilities. The model was designed in light of the requirements for accuracy, expandability, and exportability. The methodology forecasts absolute increments in local and regional growth on an annual or biennial basis and transforms these parameters into estimates of the affected area's ability to accommodate growth-induced demands, especially demands for public services. (HLW)

  11. 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.

  12. 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

  13. Regressional modeling and forecasting of economic growth for arkhangelsk region

    Directory of Open Access Journals (Sweden)

    Robert Mikhailovich Nizhegorodtsev

    2012-12-01

    Full Text Available The regression models of GRP, considering the impact of three main factors: investment in fixed assets, wages amount, and, importantly, the innovation factor –the expenditures for research and development, are constructed in this paper on the empirical data for Arkhangelsk region. That approach permits to evaluate explicitly the contribution of innovation to economic growth. Regression analysis is the main research instrument, all calculations areperformedin the Microsoft Excel. There were made meaningful conclusions regarding the potential of the region's GRP growth by various factors, including impacts of positive and negative time lags. Adequate and relevant models are the base for estimation and forecasting values of the dependent variable (GRP and evaluating their confidence intervals. The invented method of research can be used in factor assessment and prediction of regional economic growth, including growth by expectations.

  14. Three Ingredients for Improved Global Aftershock Forecasts: Tectonic Region, Time-Dependent Catalog Incompleteness, and Inter-Sequence Variability

    Science.gov (United States)

    Page, M. T.; Hardebeck, J.; Felzer, K. R.; Michael, A. J.; van der Elst, N.

    2015-12-01

    Following a large earthquake, seismic hazard can be orders of magnitude higher than the long-term average as a result of aftershock triggering. Due to this heightened hazard, there is a demand from emergency managers and the public for rapid, authoritative, and reliable aftershock forecasts. In the past, USGS aftershock forecasts following large, global earthquakes have been released on an ad-hoc basis with inconsistent methods, and in some cases, aftershock parameters adapted from California. To remedy this, we are currently developing an automated aftershock product that will generate more accurate forecasts based on the Reasenberg and Jones (Science, 1989) method. To better capture spatial variations in aftershock productivity and decay, we estimate regional aftershock parameters for sequences within the Garcia et al. (BSSA, 2012) tectonic regions. We find that regional variations for mean aftershock productivity exceed a factor of 10. The Reasenberg and Jones method combines modified-Omori aftershock decay, Utsu productivity scaling, and the Gutenberg-Richter magnitude distribution. We additionally account for a time-dependent magnitude of completeness following large events in the catalog. We generalize the Helmstetter et al. (2005) equation for short-term aftershock incompleteness and solve for incompleteness levels in the global NEIC catalog following large mainshocks. In addition to estimating average sequence parameters within regions, we quantify the inter-sequence parameter variability. This allows for a more complete quantification of the forecast uncertainties and Bayesian updating of the forecast as sequence-specific information becomes available.

  15. 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)

  16. 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

  17. 管制员需求量预测研究%Forecast Analysis on Demand of Air Traffic Controller

    Institute of Scientific and Technical Information of China (English)

    何昕; 高浩然; 陈亚青

    2011-01-01

    A series of statistical materials were collected,analyzed,and classified. Four different kinds of forecast models,namely the gray forecast model,comprehensive index method,multiple regression approach,and combined forecast model,were selected to forecast the time sequence of the demand quantity of air traffic controllers in the next 5 years in Chinese Civil Aviation. Corresponding accuracy analysis of the demand quantity was also carried out for the validity of prediction. The obtained predictive result is of great value for reference in various aspects, such as air traffic controller education and recruitment plans for the ATC institutions and authorities.%通过收集整理和分析一系列统计资料,选择灰色预测模型、灰色-马尔可夫预测模型、多元回归法和组合预测法等4种不同的人才需求量预测模型,预测出我国民航未来5年每年持照管制员需求量的时间序列,并进行了精确度分析.所得到的结果对我国民航各级管制单位制定人才引进计划,管制员培养院校制定招生计划均具有一定的参考价值.

  18. The Research of Tourism Forecasting Demand Based on BP Neural Network%基于BP神经网络的旅游需求预测研究

    Institute of Scientific and Technical Information of China (English)

    张华

    2014-01-01

    在旅游需求众多的影响因素中,旅游者的个人可支配收入情况以及旅游服务质量是影响旅游需求最为主要的因素。本文在对旅游需求影响因素进行简单分析的基础上,以国内某地区2003年-2012年10年间该地区旅游需求相关数据,采用BP神经网络技术对该地区2013年和2014年的旅游需求进行预测。%Many in the tourism demand factors,the tourists as well as personal disposable income affect the quality of tourism services most major tourism demand factors.Based on the factors affecting tourism demand on the basis of a simple analysis to a domestic region in 2003 -2012,10 years in the area of tourism demand data,using BP neural network technology to the region in 2013 and 2014 to forecast demand for travel.

  19. Investigating the Minimum Size of Study Area for an Activity-Based Travel Demand Forecasting Model

    Directory of Open Access Journals (Sweden)

    Qiong Bao

    2015-01-01

    Full Text Available Nowadays, considerable attention has been paid to the activity-based approach for transportation planning and forecasting by both researchers and practitioners. However, one of the practical limitations of applying most of the currently available activity-based models is their computation time, especially when large amount of population and detailed geographical unit level are taken into account. In this research, we investigated the possibility of restraining the size of the study area in order to reduce the computation time when applying an activity-based model, as it is often the case that only a small territory rather than the whole region is the focus of a specific study. By introducing an accuracy level of the model, we proposed in this research an iteration approach to determine the minimum size of the study area required for a target territory. In the application, we investigated the required minimum size of the study area surrounding each of the 327 municipalities in Flanders, Belgium, with regard to two different transport modes, that is, car as driver and public transport. Afterwards, a validation analysis and a case study were conducted. All the experiments were carried out by using the FEATHERS, an activity-based microsimulation modeling framework currently implemented for the Flanders region of Belgium.

  20. 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.

  1. Forecasting auroras from regional and global magnetic field measurements

    Science.gov (United States)

    Kauristie, Kirsti; Myllys, Minna; Partamies, Noora; Viljanen, Ari; Peitso, Pyry; Juusola, Liisa; Ahmadzai, Shabana; Singh, Vikramjit; Keil, Ralf; Martinez, Unai; Luginin, Alexej; Glover, Alexi; Navarro, Vicente; Raita, Tero

    2016-06-01

    We use the connection between auroral sightings and rapid geomagnetic field variations in a concept for a Regional Auroral Forecast (RAF) service. The service is based on statistical relationships between near-real-time alerts issued by the NOAA Space Weather Prediction Center and magnetic time derivative (dB/dt) values measured by five MIRACLE magnetometer stations located in Finland at auroral and sub-auroral latitudes. Our database contains NOAA alerts and dB/dt observations from the years 2002-2012. These data are used to create a set of conditional probabilities, which tell the service user when the probability of seeing auroras exceeds the average conditions in Fennoscandia during the coming 0-12 h. Favourable conditions for auroral displays are associated with ground magnetic field time derivative values (dB/dt) exceeding certain latitude-dependent threshold values. Our statistical analyses reveal that the probabilities of recording dB/dt exceeding the thresholds stay below 50 % after NOAA alerts on X-ray bursts or on energetic particle flux enhancements. Therefore, those alerts are not very useful for auroral forecasts if we want to keep the number of false alarms low. However, NOAA alerts on global geomagnetic storms (characterized with Kp values > 4) enable probability estimates of > 50 % with lead times of 3-12 h. RAF forecasts thus rely heavily on the well-known fact that bright auroras appear during geomagnetic storms. The additional new piece of information which RAF brings to the previous picture is the knowledge on typical storm durations at different latitudes. For example, the service users south of the Arctic Circle will learn that after a NOAA ALTK06 issuance in night, auroral spotting should be done within 12 h after the alert, while at higher latitudes conditions can remain favourable during the next night.

  2. Central Wind Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities: Revised Edition

    Energy Technology Data Exchange (ETDEWEB)

    Rogers, J.; Porter, K.

    2011-03-01

    The report and accompanying table addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America. The first part of the table focuses on electric utilities and regional transmission organizations that have central wind power forecasting in place; the second part focuses on electric utilities and regional transmission organizations that plan to adopt central wind power forecasting in 2010. This is an update of the December 2009 report, NREL/SR-550-46763.

  3. Study on the Forecast Method of Market Demand for Civil Aircraft%民用飞机市场需求预测方法研究

    Institute of Scientific and Technical Information of China (English)

    王晶

    2013-01-01

    民用飞机市场预测包含需求(航空运量)预测和供应(飞机机队)预测两个部分,其中需求预测为自上而下的预测,是对航空市场宏观运量进行分析和预测,这部分预测在预测模型中起着非常重要的作用。针对民用飞机市场需求预测方法和流程进行了详细的论述,该方法是大型客机市场预测和市场研究工作的基础。%The civil aircraft market forecast includes two parts:demand( aviation traffic) forecast and supply( air-craft fleet) forecast. The demand forecast is top-down forecast, which is the analysis and forecast of aviation market macroscopic traffic, and this part of forecast is very important role in the forecast model. The forecast method and process of market demand for civil aviation are discussed in detail in this paper. The method is the basic of market forecast and market research for commercial airliner.

  4. Middle Atlantic Bight Marine Ecosystem: A Regional Forecast Model Study

    Science.gov (United States)

    Kim, H.; Coles, V. J.; Garraffo, Z. D.

    2011-12-01

    Changes in basin scale climate patterns can drive changes in mesoscale physical oceanographic processes and subsequent alterations of ecosystem states. Climatic variability can be induced in the northeastern shelfbreak large marine ecosystem by climate oscillations, such as North Atlantic Oscillation, Atlantic Multidecadal Oscillation; and long-term trends, such as a warming pattern. Short term variability can be induced by changes in the water masses in the northern and southern boundaries, by Gulf Stream path and transport variations, and by local mesoscale and submesoscale features. A coupled bio-physical model (HYbrid Coordinate Ocean Model) is being used to forecast the evolution of the frontal and current systems of the shelf and Gulf Stream, and subsequent changes in thermal conditions and ecosystem structure over the Middle Atlantic Bight (MAB). This study aims to forecast the ocean state and nutrients in the MAB, and to investigate how cross-shelf exchanges of different water masses could affect nutrient budgets, primary and secondary production, and fish populations in coastal and shelf marine ecosystems. Preliminary results are shown for a regional MAB model nested to the global 1/12o HYCOM run at NOAA/NCEP/EMC using Naval Oceanographic Office (NAVO) daily initialization. Elements of this simulation are nutrient influx condition at the northern and southern boundaries through regression to ocean thermodynamic variables, and nutrient input at the river mouths.

  5. 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

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

    Science.gov (United States)

    2010-01-01

    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 of adoption of new

  7. Data-Driven Techniques for Regional Groundwater Level Forecasts

    Science.gov (United States)

    Chang, F. J.; Chang, L. C.; Tsai, F. H.; Shen, H. Y.

    2015-12-01

    Data-Driven Techniques for Regional Groundwater Level Forecasts Fi-John Changa, Li-Chiu Changb, Fong He Tsaia, Hung-Yu Shenba Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC. b Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan, ROC..Correspondence to: Fi-John Chang (email: changfj@ntu.edu.tw)The alluvial fan of the Zhuoshui River in Taiwan is a good natural recharge area of groundwater. However, the over extraction of groundwater occurs in the coastland results in serious land subsidence. Groundwater systems are heterogeneous with diverse temporal-spatial patterns, and it is very difficult to quantify their complex processes. Data-driven methods can effectively capture the spatial-temporal characteristics of input-output patterns at different scales for accurately imitating dynamic complex systems with less computational requirements. In this study, we implement various data-driven methods to suitably predict the regional groundwater level variations for making countermeasures in response to the land subsidence issue in the study area. We first establish the relationship between regional rainfall, streamflow as well as groundwater levels and then construct intelligent groundwater level prediction models for the basin based on the long-term (2000-2013) regional monthly data sets collected from the Zhuoshui River basin. We analyze the interaction between hydrological factors and groundwater level variations; apply the self-organizing map (SOM) to obtain the clustering results of the spatial-temporal groundwater level variations; and then apply the recurrent configuration of nonlinear autoregressive with exogenous inputs (R-NARX) to predicting the monthly groundwater levels. As a consequence, a regional intelligent groundwater level prediction model can be constructed based on the adaptive results of the SOM. Results demonstrate that the development

  8. Demand Forecasting: DLA’S Aviation Supply Chain High Value Products

    Science.gov (United States)

    2015-04-09

    seems to dominate the pattern of product demand from FY10 to FY13. However, as discussed above, the outlier demand value in FY13 could have zapped out...seems to dominate the pattern of product demand, and on the other, an extreme outlier value that could zap out some of the FY14 product demand. We

  9. 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.

  10. Climate change and its role in forecasting energy demand in buildings: A case study of Douala City, Cameroon

    Indian Academy of Sciences (India)

    Modeste Kameni Nematchoua; Gh R Roshan; René Tchinda; T Nasrabadi; Paola Ricciardi

    2015-02-01

    The foremost role of a building is to assure the comfort of its occupants. The thermal comfort of a building depends on the outdoor climate and requires a demand in energy for heating and cooling. In this paper, demand of energy (heating/cooling) in the buildings is discussed in Douala, Cameroon. Daily data of the last 40 years coming from five weather stations of Cameroon have been studied. Some forecasts have been carried out with 14 GCM models, associated to three future climate scenarios B1, A2, and A1B. However, only INCM3 of General Circulation Model (GCM) and A2 scenario was used. Energy demand in buildings is valued by HDD (heating degree day) and CDD (cooling degree day) indices. Obtained results show that the temperature evolves more quickly in dry season than in rainy season in Douala. Climate rise indicates an increasing demand of energy in the buildings for cooling. Global Douala heating shows a definite effect on outdoor comfort. From 2045 to 2075, the demand of energy for cooling will be superior to 50%. The total demand in energy for heating in the buildings is estimated to be 67.882 kcal from 1970 to 2000 and will be around 67.774 kcal from 2013 to 2043.

  11. 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.

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    M. Yousefi, M. Omid, Sh. Rafiee, S.F. Ghaderi

    2013-01-01

    Full Text Available 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.

  14. 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.

  15. 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.

  16. 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.

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

    DEFF Research Database (Denmark)

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

    2005-01-01

    This article 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$59 billion. The study shows with very high statistical significance...... ±20%. The result is substantial financial risks, which are typically ignored or downplayed by planners and decision makers to the detriment of social and economic welfare. The data also show that forecasts have not become more accurate over the 30-year period studied, despite claims to the contrary...

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

    DEFF Research Database (Denmark)

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

    2005-01-01

    ±20%. The result is substantial financial risks, which are typically ignored or downplayed by planners and decision makers to the detriment of social and economic welfare. The data also show that forecasts have not become more accurate over the 30-year period studied, despite claims to the contrary......This article 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$59 billion. The study shows with very high statistical significance...

  19. Bridging the Gap in Transport Project Evaluation: Accounting for the Inaccuracies in Demand Forecasts and Construction Costs Estimations

    DEFF Research Database (Denmark)

    Salling, Kim Bang; Leleur, Steen

    For decades researchers have claimed that demand forecasts and construction costs estimations are assigned with large degrees of uncertainty, commonly referred to as Optimism Bias. A severe consequence is that ex-ante socio-economic evaluation of infrastructure projects becomes inaccurate and can...... lead to unsatisfactory investment decisions. Thus there is a need for better risk assessment and decision support, which is addressed by the recently developed UNITE-DSS model. It is argued that this simulation-based model can offer decision makers new and better ways to deal with risk assessment....

  20. Retrospective forecasting test of a statistical physics model for earthquakes in Sichuan-Yunnan region

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Pattern informatics (PI) model is one of the recently developed predictive models of earthquake phys- ics based on the statistical mechanics of complex systems. In this paper, retrospective forecast test of the PI model was conducted for the earthquakes in Sichuan-Yunnan region since 1988, exploring the possibility to apply this model to the estimation of time-dependent seismic hazard in continental China. Regional earthquake catalogue down to ML3.0 from 1970 to 2007 was used. The ‘target magnitude’ for the forecast test was MS5.5. Fifteen-year long ‘sliding time window’ was used in the PI calculation, with ‘anomaly training time window’ being 5 years and ‘forecast time window’ being 5 years, respectively. Receiver operating characteristic (ROC) test was conducted for the evaluation of the forecast result, showing that the PI forecast outperforms not only random guess but also the simple number counting approach based on the clustering hypothesis of earthquakes (the RI forecast). If the ‘forecast time window’ was shortened to 3 years and 1 year, respectively, the forecast capability of the PI model de- creased significantly, albeit outperformed random forecast. For the one year ‘forecast time window’, the PI result was almost comparable to the RI result, indicating that clustering properties play a more important role at this time scale.

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

    Science.gov (United States)

    2016-06-01

    Hendricks and Robey (1936) explain the coefficient of variation as the ratio of the standard deviation of a number of measurements to their arithmetic mean...forecasting: A handbook for researchers and practitioners (Vol. 30). New York: Springer Science & Business Media . Armstrong, J. S., & Collopy, F. (1992

  2. 基于时间序列法的北京市需水量预报%Water demand forecasting of Beijing using the Time Series Forecasting Method

    Institute of Scientific and Technical Information of China (English)

    ZHAI Yuanzheng; WANG Jinsheng; TENG Yanguo; ZUO Rui

    2012-01-01

    @@%It is essential to establish the water resources exploitation and utilization planning,which is mainly based on recognizing and forecasting the water consumed structure rationally and scientifically.During the past 30 years (1980-2009),mean annual precipitation and total water resource of Beijing have decreased by 6.89% and 31.37% compared with those perennial values,respectively,while total water consumption during the same period reached pinnacle historically.Accordingly,it is of great significance for the harmony between socio-economic development and environmental development.Based on analyzing total water consumption,agricultural,industrial,domestic and environmental water consumption,and evolution of water consumed structure,further driving forces of evolution of total water consumption and water consumed structure are revealed systematically.Prediction and discussion are achieved for evolution of total water consumption,water consumed structure,and supply-demand situation of water resource in the near future of Beijing using Time Series Forecasting Method.The purpose of the endeavor of this paper is to provide scientific basis for the harmonious development between socio-economy and water resources,for the establishment of rational strategic planning of water resources,and for the social sustainable development of Beijing with scientific bases.

  3. 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

  4. Translating the potential of hydrological forecasts into improved decision making in African regions

    Science.gov (United States)

    Sheffield, J.; He, X.; Wanders, N.; Wood, E. F.; Ali, A.; Olang, L.; Estes, L. D.; Caylor, K. K.; Evans, T. P.

    2015-12-01

    Hydrological forecasts at local scale and seasonal time scales have the potential to inform decision-making by individuals and institutions to improve management of water resources and enhance food security. Much progress has been made in recent years in understanding climate variability and its predictability over African regions. However, there remain many challenges in translating large-scale evaluations and forecasts into locally relevant information. This is hampered by lack of on the ground data of hydrological and agricultural states, and the generally low skill of climate forecasts at time scales beyond one or two weeks. Additionally, the uptake of forecasts is not prevalent because of lack of capacity, and institutional and cultural barriers to using new and uncertain information. New technologies for monitoring and forecasting relevant hydrological variables, and novel approaches to understanding how this information may be used within decision making processes, have the potential to make substantial progress in addressing these challenges. We present a quasi-operational drought and flood monitoring and forecasting system and its use in understanding the potential of hydrological forecasts for improved decision-making. The system monitors in near real-time the terrestrial water cycle for the African continent based on remote sensing data and land surface hydrological modeling. The monitoring forms initial conditions for hydrological forecasts at short time scale, aimed at flood forecasting, and seasonal scale aimed at drought and crop yield forecasts. The flood forecasts are driven by precipitation and temperature forecasts from the Global Forecast System (GFS). The drought forecasts are driven by climate forecasts from the North American Multi-Model Ensemble (NMME). The seasonal forecast skill is modest and seasonally/regionally dependent with part of the skill coming from persistence in initial land surface conditions. We discuss the use of the system

  5. Confronting the demand and supply of snow seasonal forecasts for ski resorts : the case of French Alps

    Science.gov (United States)

    Dubois, Ghislain

    2017-04-01

    Alpine ski resorts are highly dependent on snow, which availability is characterized by a both a high inter-annual variability and a gradual diminution due to climate change. Due to this dependency to climatic resources, the ski industry is increasingly affected by climate change: higher temperatures limit snow falls, increase melting and limit the possibilities of technical snow making. Therefore, since the seventies, managers drastically improved their practices, both to adapt to climate change and to this inter-annual variability of snow conditions. Through slope preparation and maintenance, snow stock management, artificial snow making, a typical resort can approximately keep the same season duration with 30% less snow. The ski industry became an activity of high technicity The EUPORIAS FP7 (www.euporias.eu) project developed between 2012 and 2016 a deep understanding of the supply and demand conditions for the provision of climate services disseminating seasonal forecasts. In particular, we developed a case study, which allowed conducting several activities for a better understanding of the demand and of the business model of future services applied to the ski industry. The investigations conducted in France inventoried the existing tools and databases, assessed the decision making process and data needs of ski operators, and provided evidences that some discernable skill of seasonal forecasts exist. This case study formed the basis of the recently funded PROSNOW H2020 project. We will present the main results of EUPORIAS project for the ski industry.

  6. Power Demand Forecast and Development Strategy for the Period of 2005-2030

    Institute of Scientific and Technical Information of China (English)

    Wu Jingru; Jin Wen

    2007-01-01

    @@ Learning from lessons of underestimating the economic growth and misunderstanding electric elasticity factor in the power forecast conducted a few years ago,in additi on tobroadly referring to the situations of power economic growth of developed countries when they were in industrial developing stage,this report is presented with higher realistic reference value and academic value,and has attracted common attention of decision-makers and researchers in Chinese power industry.

  7. Experimental weekly to seasonal U.S. forecasts with the Regional Spectral Model

    Science.gov (United States)

    J. Roads

    2004-01-01

    As described previously Roads et al. 2001a, hereafter RCF), the Scripps Experimental Climate Prediction Center (ECPC) has been making routine, near-real-time, long-range experimental global and regional dynamical forecasts since 27 September 1997. The global spectral model (GSM) used for these forecasts is that of National Centers for Environmental Prediction’s (NCEP;...

  8. 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

  9. Verification of operational solar flare forecast: Case of Regional Warning Center Japan

    Science.gov (United States)

    Kubo, Yûki; Den, Mitsue; Ishii, Mamoru

    2017-08-01

    In this article, we discuss a verification study of an operational solar flare forecast in the Regional Warning Center (RWC) Japan. The RWC Japan has been issuing four-categorical deterministic solar flare forecasts for a long time. In this forecast verification study, we used solar flare forecast data accumulated over 16 years (from 2000 to 2015). We compiled the forecast data together with solar flare data obtained with the Geostationary Operational Environmental Satellites (GOES). Using the compiled data sets, we estimated some conventional scalar verification measures with 95% confidence intervals. We also estimated a multi-categorical scalar verification measure. These scalar verification measures were compared with those obtained by the persistence method and recurrence method. As solar activity varied during the 16 years, we also applied verification analyses to four subsets of forecast-observation pair data with different solar activity levels. We cannot conclude definitely that there are significant performance differences between the forecasts of RWC Japan and the persistence method, although a slightly significant difference is found for some event definitions. We propose to use a scalar verification measure to assess the judgment skill of the operational solar flare forecast. Finally, we propose a verification strategy for deterministic operational solar flare forecasting. For dichotomous forecast, a set of proposed verification measures is a frequency bias for bias, proportion correct and critical success index for accuracy, probability of detection for discrimination, false alarm ratio for reliability, Peirce skill score for forecast skill, and symmetric extremal dependence index for association. For multi-categorical forecast, we propose a set of verification measures as marginal distributions of forecast and observation for bias, proportion correct for accuracy, correlation coefficient and joint probability distribution for association, the

  10. Demand Forecasting and Revenue Requirements, with Implications for Consideration in British Columbia,

    Science.gov (United States)

    1983-05-01

    Econometric Study of Electricity Demand by Manufacturing Industries," NUREG /CR-11358, Oak Ridge, Tennessee: Energy Division, Oak Ridge National...Load for States and Utility Service Areas," NUREG /CR-2692, ORNL/TM- 7947, Oak Ridge, Tennessee: Energy Division, Oak Ridge National Laboratory, May...1982. Just, Richard E. and Chang, Hui S., "A Varying Elasticity Model of Electricity Demand with Given Appliance Saturation," NUREG /CR- 1956, ORNL/ NUREG

  11. Gaining Benefits from Joint Forecasting and Replenishment Processes: The Case of Auto-Correlated Demand

    OpenAIRE

    Yossi Aviv

    2002-01-01

    In this paper we consider a cooperative, two-level supply chain consisting of a retailer and a supplier. As in many practical settings, the supply chain members progressively observe market signals that enable them to explain future demand. The demand itself evolves according to an auto-regressive time series. We examine three types of supply chain configurations. In the first setting, the retailer and the supplier coordinate their policy parameters in an attempt to minimize systemwide costs,...

  12. Forecasting, Forecasting

    Science.gov (United States)

    Michael A. Fosberg

    1987-01-01

    Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

  13. Development of multimodel ensemble based district level medium range rainfall forecast system for Indian region

    Indian Academy of Sciences (India)

    S K Roy Bhowmik; V R Durai

    2012-04-01

    India Meteorological Department has implemented district level medium range rainfall forecast system applying multimodel ensemble technique, making use of model outputs of state-of-the-art global models from the five leading global NWP centres. The pre-assigned grid point weights on the basis of anomaly correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.25° × 0.25° utilizing two season datasets (1 June–30 September, 2007 and 2008) and the multimodel ensemble forecasts (day-1 to day-5 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district, taking the average value of all grid points falling in a particular district. In this paper, we describe the development strategy of the technique and performance skill of the system during summer monsoon 2009. The study demonstrates the potential of the system for improving rainfall forecasts at five days time scale over Indian region. Districtwise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most states of the country, particularly over the states where the monsoon systems are more dominant.

  14. 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.

  15. STUDY OF THE EFFECTS OF REDUCING SYSTEMATIC ERRORS ON MONTHLY REGIONAL CLIMATE DYNAMICAL FORECAST

    Institute of Scientific and Technical Information of China (English)

    ZENG Xin-min; XI Chao-li

    2009-01-01

    A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model tbr monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system,and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC),U.S.A.,which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surthce air temperature,it is found that the corrected forecast is apparently better than the original,suggesting that the approach can be applied for improving monthly-scale regional climate dynamical lbrecast.

  16. 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.

  17. [Research on the forecasting of trends in demand for pharmacists 2011-2035].

    Science.gov (United States)

    Hasegawa, Yoichi; Sakamaki, Hiroyuki; Yamazaki, Manabu; Iwatsuki, Susumu; Oide, Sentaro; Kitada, Mitsukazu; Ohashi, Yoshiaki; Suda, Kohji; Mochizuki, Masataka

    2014-01-01

    The first crop of pharmacists graduating from 6-year programs in pharmaceutical l education arrived in April 2012, and it will be important to incorporate new factors when predicting future trends in supply and demand for pharmacists. If we project supply given an exam pass rate of 75%, the supply of pharmacists will increase for the next 10 years or so if the number of exam takers is about 10000, and no decrease in the total number of pharmacists is expected until 2035. At pharmacies, a high degree of demand for the services of pharmacists can be expected to result from increases in the number of elderly patients and the number of patients receiving prescriptions, together with expanded accommodation of home health care, if the proportion of prescriptions that are actually filled up to 70%. At hospitals, demand has been projected to increase over the short term, owing to such factors as the trend toward having a resident pharmacist in each ward, advances in team medicine, and the spread of outpatient chemotherapy. Given the rising enrollment quotas for schools of pharmacy, and if the current supply and demand for pharmacists are maintained, we cannot rule out the possibility that pharmacists will come to be in excess supply within a 10-year horizon if the number of unemployed continues to decrease and the employment rate continues to improve along with changes in economic conditions and the consciousness of graduates of the 6-year programs.

  18. Implications of the Regional Earthquake Likelihood Models test of earthquake forecasts in California

    Directory of Open Access Journals (Sweden)

    Michael Karl Sachs

    2012-09-01

    Full Text Available The Regional Earthquake Likelihood Models (RELM test was the first competitive comparison of prospective earthquake forecasts. The test was carried out over 5 years from 1 January 2006 to 31 December 2010 over a region that included all of California. The test area was divided into 7682 0.1°x0.1° spatial cells. Each submitted forecast gave the predicted numbers of earthquakes Nemi larger than M=4.95 in 0.1 magnitude bins for each cell. In this paper we present a method that separates the forecast of the number of test earthquakes from the forecast of their locations. We first obtain the number Nem of forecast earthquakes in magnitude bin m. We then determine the conditional probability λemi=Nemi/Nem that an earthquake in magnitude bin m will occur in cell i. The summation of λemi over all 7682 cells is unity. A random (no skill forecast gives equal values of λemi for all spatial cells and magnitude bins. The skill of a forecast, in terms of the location of the earthquakes, is measured by the success in assigning large values of λemi to the cells in which earthquakes occur and low values of λemi to the cells where earthquakes do not occur. Thirty-one test earthquakes occurred in 27 different combinations of spatial cells i and magnitude bins m, we had the highest value of λemi for that mi cell. We evaluate the performance of eleven submitted forecasts in two ways. First, we determine the number of mi cells for which the forecast λemi was the largest, the best forecast is the one with the highest number. Second, we determine the mean value of λemi for the 27 mi cells for each forecast. The best forecast has the highest mean value of λemi. The success of a forecast during the test period is dependent on the allocation of the probabilities λemi between the mi cells, since the sum over the mi cells is unity. We illustrate the forecast distributions of λemi and discuss their differences. We conclude that the RELM test was successful in

  19. Multiple Model Demand Forecasting Compared to Air Force Logistics Command D062 Performance.

    Science.gov (United States)

    1980-06-01

    9132. 9142. 0633. 9077. 9402. 1240.7 -1056.5 METHOD SIL 5 4 1 2 4 3 2 5 3 FOCUS ORC 13485 . 10331. 7383. 10695. 10316. 9610. 14765. 10346. 10331. 1592.9...465. 100.4 24.1 RAU QUARTERLY INPUT D614 149. 143. iou. 97. 53. 153. 5. 04. 198. :32. 66. 76. 0?. 127. 226. 117. 94. 148. 76. IsO . FORECASTS 365(D 04...0 154. 184. 197. 188. 178. 163. ISO . 200. 199. 16.7 -1.0 E , 9 1,: ;71. 1 3 175. 176. 178. ?14. 175. 177. 1. 6.0 -6.6 %P1T .414 173. 173. 193. 180

  20. Alaska North Slope regional gas hydrate production modeling forecasts

    Science.gov (United States)

    Wilson, S.J.; Hunter, R.B.; Collett, T.S.; Hancock, S.; Boswell, R.; Anderson, B.J.

    2011-01-01

    A series of gas hydrate development scenarios were created to assess the range of outcomes predicted for the possible development of the "Eileen" gas hydrate accumulation, North Slope, Alaska. Production forecasts for the "reference case" were built using the 2002 Mallik production tests, mechanistic simulation, and geologic studies conducted by the US Geological Survey. Three additional scenarios were considered: A "downside-scenario" which fails to identify viable production, an "upside-scenario" describes results that are better than expected. To capture the full range of possible outcomes and balance the downside case, an "extreme upside scenario" assumes each well is exceptionally productive.Starting with a representative type-well simulation forecasts, field development timing is applied and the sum of individual well forecasts creating the field-wide production forecast. This technique is commonly used to schedule large-scale resource plays where drilling schedules are complex and production forecasts must account for many changing parameters. The complementary forecasts of rig count, capital investment, and cash flow can be used in a pre-appraisal assessment of potential commercial viability.Since no significant gas sales are currently possible on the North Slope of Alaska, typical parameters were used to create downside, reference, and upside case forecasts that predict from 0 to 71??BM3 (2.5??tcf) of gas may be produced in 20 years and nearly 283??BM3 (10??tcf) ultimate recovery after 100 years.Outlining a range of possible outcomes enables decision makers to visualize the pace and milestones that will be required to evaluate gas hydrate resource development in the Eileen accumulation. Critical values of peak production rate, time to meaningful production volumes, and investments required to rule out a downside case are provided. Upside cases identify potential if both depressurization and thermal stimulation yield positive results. An "extreme upside

  1. Model complex of forecasting of interdependent development of migration processes and region labour market

    Directory of Open Access Journals (Sweden)

    Valery Aleksandrovich Chereshnev

    2013-09-01

    Full Text Available The essential problems of current international labor migration raising the need to forecast interdependent labor market and migration processes in a region for improving the effectiveness of regional migration policy in Russia are considered. A model for the prediction of migration flows as determined by wage differentials, distances between populations of the regions as well as wages and unemployment, which come from the impact of migration on the availability of jobs at the labor market with search-matching frictions for source and host regions is presented in the framework of search and matching theory. Applying the model to statistical data, the forecast for labor migration flows to regions of Russia from CIS countries, as well as its effects on regional labor markets for 2012-2021 is maid. Recommendations for improving the effectiveness of regional migration policy are given on the basis of the forecast.

  2. 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

    1991-12-31

    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.

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

    Science.gov (United States)

    Fernández-Vázquez, Esteban; Moreno, Blanca

    2017-08-01

    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. 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.

  5. A Survey of Water Demand Forecasting Procedures on Fixed Army Installations.

    Science.gov (United States)

    1985-02-01

    Fort Dix, New Jersey Fort Hamilton, New York Fort Eustis, Virginia Fort Pickett, Virginia Fort Gordon, Georgia Fort Rucker, Alabama Fort Benjamin ...Vol 15, No. 4 (August 1979), pp 763-767. Darr , P., S. L. Feldman, and C. Komen, The Demand for Urban Water (Leiden, the Netherlands: Martinus...Fort Knox 5090 6635 -1545 628,6 -1196 Fort Leavenworth 2055 2070 -15 2144 -90 Fort Benjamin Harrison 552 2656 -2104 1554 -1001 Fort Lee 1655 2878 -1223

  6. Comparison of two analog-based downscaling methods for regional reference evapotranspiration forecasts

    Science.gov (United States)

    Tian, Di; Martinez, Christopher J.

    2012-12-01

    SummaryThe objective of this study was to compare the performance of natural analog (NA) and constructed analog (CA) methods to produce both probabilistic and deterministic downscaled daily reference evapotranspiration (ETo) forecasts in the southeastern United States. The 1-15 day, 15-member ETo forecasts were produced from 1979 to 2009 using the Penman-Monteith equation and a forecast analog approach with a combination of the Global Forecast System (GFS) reforecasts and NCEP-DOE Reanalysis 2 climatology, and were downscaled using the North American Regional Reanalysis (NARR). The Pearson correlation coefficient (R), mean squared error skill score (MSESS), and Bias were used to evaluate the skill of downscaled deterministic forecasts. The Linear Error in Probability Space (LEPS) skill score, Brier Skill Score (BSS), relative operating characteristic, and reliability diagrams were used to evaluate the skill of downscaled probabilistic forecasts. Overall, CA showed slightly higher skill than NA in terms of the metrics for deterministic forecasts, while for probabilistic forecasts NA showed higher skill than CA regarding the BSS in five categories (terciles, and 10th and 90th percentiles) and lower skill than CA regarding the LEPS skill score. Both CA and NA produced skillful deterministic results in the first 3 lead days, while the skill was higher for CA than for NA. Probabilistic NA forecasts exhibited higher resolution and reliability than CA, likely due to a larger ensemble size. Forecasts by both methods showed the lowest skill in the Florida peninsula and in mountainous areas, likely due to the fact that these features were not well-resolved in the model forecast.

  7. 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.

  8. 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

  9. 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.

  10. Sequential correction of ensemble regional weather predictions for forecasting reference evapotranspiration

    Science.gov (United States)

    Pelosi, Anna; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2016-04-01

    This study explores the performance of an adaptive procedure for correcting the ensemble numerical weather outputs applied to the probabilistic forecast of reference evapotranspiration (ETo). This procedure is proposed as an effective forecast correction method when the available dataset is not large enough for the calibration of statistical batch procedures. The numerical weather prediction outputs are those provided by COSMO-LEPS, an ensemble-based Limited Area Model, with 16 members and 7.5 km spatial resolution, with forecast lead-time up to 5 days. ETo forecasts are computed according to the FAO Penman-Monteith (FAO-PM) equation, which requires data of five weather variables: air temperature, relative humidity, solar radiation and wind speed. The performance of the proposed procedure is evaluated at eighteen monitoring stations, located in Campania region (Southern Italy), with two alternative strategies: i) correction applied to the raw ensemble forecasts of the five weather variables prior applying the FAO-PM equation; ii) correction applied to the ensemble output of the ETo forecasts obtained with FAO-PM equation after using the raw ensemble weather forecasts as input. In both cases the suggested post-processing procedure was able to significantly increase the accuracy and reduce the uncertainty of the ETo forecasts.

  11. Activities of the Climate Forecast Unit (CFU) on regional decadal prediction

    Science.gov (United States)

    Guemas, V.; Prodhomme, C.; Doblas-Reyes, F.; Volpi, D.; Caron, L. P.; Davis, M.; Menegoz, M.; Saurral, R. I.; Bellprat, O.

    2014-12-01

    The Climate Forecasting Unit (CFU) is a research unit devoted to develop climate forecast systems to contribute to the creation of climate services that aims to 1) develop climate forecast systems and prediction methodologies, 2) investigate the potential sources of skill and understand the limitation of state-of-the-art forecast systems, 3) formulate reliable climate forecasts that meet specific user needs and 4) contribute to the development of climate services. This presentation will provide an overview of the latest results of this research unit in the field of regional decadal prediction focusing on 1) an assessment of the relative merits of the full-field and the anomaly initialisation techniques, 2) a description of the forecast quality of North Atlantic tropical cyclone activity and South Pacific climate, 3) an evaluation of the impact of volcanic aerosol prescription during decadal forecasts, and 4) the strategy for the development of a climate service to ensure that forecasts are both useful and action-oriented. Results from several European projects, SPECS, PREFACE and EUPORIAS, will be used to illustrate these findings.

  12. Research on demand forecasting method and application of inland waterway shoreline resources%内河航道岸线资源需求预测方法及应用探究

    Institute of Scientific and Technical Information of China (English)

    李欢欢; 吴凤平

    2013-01-01

      目前国内对于内河航道岸线需求预测主要采取定性分析的方法,科学性和有效性较低,直接影响资源的合理规划。针对该方面的不足,提出基于灰色预测方法和回归分析方法的组合预测方法对内河航道岸线需求量进行预测,并进行实证研究,实证表明运用现代科技方法能够提高预测的精度。%  At present, demand forecasting of inland waterway shoreline resources mainly adopts the method of qualitative analysis in domestic. It’s low scientific and effective directly affect the rationality of resources planning. Aim to the insufficiency, puts forward the method of combination forecast based on grey prediction method and regression analysis method, to forecast the demand of inland waterway shoreline resources. With forecasting of port coastline of Nanjing, zhenjiang, Yangzhou, taizhou region as an example for empirical research. Empirical shows that using the modern technology method can improve the accuracy of prediction.

  13. Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

    Science.gov (United States)

    Roberts, J. Brent; Roberts, Franklin R.

    2013-01-01

    The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

  14. Research activities at the Australian Bureau of Meteorology for the regional ionospheric specification and forecasting

    Science.gov (United States)

    Bouya, Zahra; Terkildsen, Michael

    2016-07-01

    The Australian Space Forecast Centre (ASFC) provides space weather forecasts to a diverse group of customers. Space Weather Services (SWS) within the Australian Bureau of Meteorology is focussed both on developing tailored products and services for the key customer groups, and supporting ASFC operations. Research in SWS is largely centred on the development of data-driven models using a range of solar-terrestrial data. This paper will cover some data requirements , approaches and recent SWS activities for data driven modelling with a focus on the regional Ionospheric specification and forecasting.

  15. 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…

  16. The role of component-wise boosting for regional economic forecasting

    OpenAIRE

    Lehmann, Robert; Wohlrabe, Klaus

    2015-01-01

    This paper applies component-wise boosting to the topic of regional economic forecasting. By using unique quarterly gross domestic product data for one German state for the period from 1996 to 2013, in combination with a large data set of 253 monthly indicators, we show how accurate forecasts obtained from component-wise boosting are compared to a simple benchmark model. We additionally take a closer look into the algorithm and evaluate whether a stable pattern of selected indicators exists o...

  17. Verification of a probabilistic flood forecasting system for an Alpine Region of northern Italy

    Science.gov (United States)

    Laiolo, P.; Gabellani, S.; Rebora, N.; Rudari, R.; Ferraris, L.; Ratto, S.; Stevenin, H.

    2012-04-01

    Probabilistic hydrometeorological forecasting chains are increasingly becoming an operational tool used by civil protection centres for issuing flood alerts. One of the most important requests of decision makers is to have reliable systems, for this reason an accurate verification of their predictive performances become essential. The aim of this work is to validate a probabilistic flood forecasting system: Flood-PROOFS. The system works in real time, since 2008, in an alpine Region of northern Italy, Valle d'Aosta. It is used by the Civil Protection regional service to issue warnings and by the local water company to protect its facilities. Flood-PROOFS uses as input Quantitative Precipitation Forecast (QPF) derived from the Italian limited area model meteorological forecast (COSMO-I7) and forecasts issued by regional expert meteorologists. Furthermore the system manages and uses both real time meteorological and satellite data and real time data on the maneuvers performed by the water company on dams and river devices. The main outputs produced by the computational chain are deterministic and probabilistic discharge forecasts in different cross sections of the considered river network. The validation of the flood prediction system has been conducted on a 25 months period considering different statistical methods such as Brier score, Rank histograms and verification scores. The results highlight good performances of the system as support system for emitting warnings but there is a lack of statistics especially for huge discharge events.

  18. FORECAST OF PRODUCTION OF FLEX FUEL CARS AND ETHANOL DEMAND IN BRAZIL IN 2014

    Directory of Open Access Journals (Sweden)

    Felipe Teixeira Favaro

    2010-08-01

    Full Text Available Brazil is currently set up as an important worldwide example in relation to the successful incorporation of biofuel ethanol in its energy matrix. The article examines the impacts of biofuel technology insertion (flex fuel and estimated, using the methodology Fisher-Pry, the evolution of the total vehicle fleet with this technology in Brazil in 2014. In addition, we performed a prediction of future demand for ethanol, using the technique of Gompertz, also for the year 2014. Both methodologies were checked through STATA statistical system. The study relied on data from January 2005 to October 2009, which were extrapolated to December 2014. The Fisher-Pry model was used to prepare the scenario analysis of the replacement of cars with conventional technology (gasoline, for cars with flex fuel technology. As a result, the projection showed an increase of 18.2 percentage points in five years, rising from 78.4% recorded in October 2009, to 96.6% in December 2014. As a premise, we considered the continuity of the prices of ethanol and gasoline observed between jan/02 Oct/2009 and that shows ethanol being marketed at a price below the minimum necessary to make this renewable fuel competitive with gasoline in 88 of 95 months. The Gompertz model indicated that ethanol will represent approximately 43.9% of all fuel sold by distributors at the end of 2014, whereas today this share is only 18.2%.

  19. Real-Time CME Forecasting Using HMI Active-Region Magnetograms and Flare History

    Science.gov (United States)

    Falconer, David; Moore, Ron; Barghouty, Abdulnasser F.; Khazanov, Igor

    2011-01-01

    We have recently developed a method of predicting an active region s probability of producing a CME, an X-class Flare, an M-class Flare, or a Solar Energetic Particle Event from a free-energy proxy measured from SOHO/MDI line-of-sight magnetograms. This year we have added three major improvements to our forecast tool: 1) Transition from MDI magnetogram to SDO/HMI magnetogram allowing us near-real-time forecasts, 2) Automation of acquisition and measurement of HMI magnetograms giving us near-real-time forecasts (no older than 2 hours), and 3) Determination of how to improve forecast by using the active region s previous flare history in combination with its free-energy proxy. HMI was turned on in May 2010 and MDI was turned off in April 2011. Using the overlap period, we have calibrated HMI to yield what MDI would measure. This is important since the value of the free-energy proxy used for our forecast is resolution dependent, and the forecasts are made from results of a 1996-2004 database of MDI observations. With near-real-time magnetograms from HMI, near-real-time forecasts are now possible. We have augmented the code so that it continually acquires and measures new magnetograms as they become available online, and updates the whole-sun forecast from the coming day. The next planned improvement is to use an active region s previous flare history, in conjunction with its free-energy proxy, to forecast the active region s event rate. It has long been known that active regions that have produced flares in the past are likely to produce flares in the future, and that active regions that are nonpotential (have large free-energy) are more likely to produce flares in the future. This year we have determined that persistence of flaring is not just a reflection of an active region s free energy. In other words, after controlling for free energy, we have found that active regions that have flared recently are more likely to flare in the future.

  20. Forecasting Urban Water Demand via Machine Learning Methods Coupled with a Bootstrap Rank-Ordered Conditional Mutual Information Input Variable Selection Method

    Science.gov (United States)

    Adamowski, J. F.; Quilty, J.; Khalil, B.; Rathinasamy, M.

    2014-12-01

    This paper explores forecasting short-term urban water demand (UWD) (using only historical records) through a variety of machine learning techniques coupled with a novel input variable selection (IVS) procedure. The proposed IVS technique termed, bootstrap rank-ordered conditional mutual information for real-valued signals (brCMIr), is multivariate, nonlinear, nonparametric, and probabilistic. The brCMIr method was tested in a case study using water demand time series for two urban water supply system pressure zones in Ottawa, Canada to select the most important historical records for use with each machine learning technique in order to generate forecasts of average and peak UWD for the respective pressure zones at lead times of 1, 3, and 7 days ahead. All lead time forecasts are computed using Artificial Neural Networks (ANN) as the base model, and are compared with Least Squares Support Vector Regression (LSSVR), as well as a novel machine learning method for UWD forecasting: the Extreme Learning Machine (ELM). Results from one-way analysis of variance (ANOVA) and Tukey Honesty Significance Difference (HSD) tests indicate that the LSSVR and ELM models are the best machine learning techniques to pair with brCMIr. However, ELM has significant computational advantages over LSSVR (and ANN) and provides a new and promising technique to explore in UWD forecasting.

  1. Feasibility of large-scale water monitoring and forecasting in the Asia-Pacific region

    Science.gov (United States)

    van Dijk, A. I. J. M.; Peña-Arancibia, J. L.; Sardella, C. S. E.

    2012-04-01

    The Asian-Pacific region (including China, India and Pakistan) is home to 51% of the global population. It accounts for 53% of agricultural and 32% of domestic water use world wide. Due to the influence of Pacific Ocean and Indian Ocean circulation patterns, the region experiences strong inter-annual variations in water availability and occurrence of drought, flood and severe weather. Some of the countries in the region have national water monitoring or forecasting systems, but they are typically of fairly narrow scope. We investigated the feasibility and utility of an integrated regional water monitoring and forecasting system for water resources, floods and drought. In particular, we assessed the quality of information that can be achieved by relying on internationally available data sources, including numerical weather prediction (NWP) and satellite observations of precipitation, soil moisture and vegetation. Combining these data sources with a large scale hydrological model, we produced monitoring and forecast information for selected retrospective case studies. The information was compared to that from national systems, both in terms of information content and system characteristics (e.g. scope, data sources, and information latency). While national systems typically have better access to national observation systems, they do not always make effective use of the available data, science and technology. The relatively slow changing nature of important Pacific and Indian Ocean circulation patterns adds meaningful seasonal forecast skill for some regions. Satellite and NWP precipitation estimates can add considerable value to the national gauge networks: as forecasts, as near-real time observations and as historic reference data. Satellite observations of soil moisture and vegetation are valuable for drought monitoring and underutilised. Overall, we identify several important opportunities for better water monitoring and forecasting in the Asia-Pacific region.

  2. Forecasting Lake-Effect Precipitation in the Great Lakes Region Using NASA Enhanced-Satellite Data

    Science.gov (United States)

    Cipullo, Michelle; Molthan, Andrew; Shafer, Jackie; Case, Jonathan; Jedlovec, Gary

    2011-01-01

    Lake-effect precipitation is common in the Great Lakes region, particularly during the late fall and winter. The synoptic processes of lake-effect precipitation are well understood by operational forecasters, but individual forecast events still present a challenge. Locally run, high resolution models can assist the forecaster in identifying the onset and duration of precipitation, but model results are sensitive to initial conditions, particularly the assumed surface temperature of the Great Lakes. The NASA Short-term Prediction Research and Transition (SPoRT) Center has created a Great Lakes Surface Temperature (GLST) composite, which uses infrared estimates of water temperatures obtained from the MODIS instrument aboard the Aqua and Terra satellites, other coarser resolution infrared data when MODIS is not available, and ice cover maps produced by the NOAA Great Lakes Environmental Research Lab (GLERL). This product has been implemented into the Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS), used within forecast offices to run local, high resolution forecasts. The sensitivity of the model forecast to the GLST product was analyzed with a case study of the Lake Effect Storm Echinacea, which produced 10 to 12 inches of snowfall downwind of Lake Erie, and 8 to 18 inches downwind of Lake Ontario from 27-29 January 2010. This research compares a forecast using the default Great Lakes surface temperatures from the Real Time Global sea surface temperature (RTG SST), in the WRF-EMS model to the enhanced NASA SPoRT GLST product to study forecast impacts. Results from this case study show that the SPoRT GLST contained less ice cover over Lake Erie and generally cooler water temperatures over Lakes Erie and Ontario. Latent and sensible heat fluxes over Lake Ontario were decreased in the GLST product. The GLST product decreased the quantitative precipitation forecast (QPF), which can be correlated to the decrease in temperatures and heat

  3. 企业需求预测准确性存在的问题及其应对策略%Problems with Enterprise Demand Forecast Accuracy and Coping Strategies

    Institute of Scientific and Technical Information of China (English)

    董鹏; 赵得龙; 杨阿南

    2014-01-01

    In view of the important role of demand forecast in enterprise operation activities and the diversity of influential factors, the method, problems with the method, process, system and management of enterprise actual demand forecast are combed and analyzed. It is pointed out that the deviation of demand forecast and future market forecast can be effectively controlled by optimizing demand forecast methods, perfecting demand forecast systems and improving demand forecast management, thereby improving the accuracy of demand forecast and promoting the sound operation of enterprise production and sales.%鉴于需求预测在企业经营活动中的重要作用及其影响因素的多样性,对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析,指出通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和对未来市场预测的偏差,从而提高需求预测的准确性,促进企业生产和销售的良性运行。

  4. 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.

  5. Forecasting changes of arid geosystems under ecological destabilizing conditions in the Aral Sea region

    Directory of Open Access Journals (Sweden)

    V.A. Rifikov

    2014-05-01

    Full Text Available We discuss the main natural and anthropogenic factors of forecasting and establish the basic tendencies to change natural complexes. We conclude that the Aral Sea and the Aral Sea region are genetically uniform and paragenetically dynamical macro geosystems. By considering properties and features of structural and dynamic conditions of superaqual, subequal, and eluvial geosystems of the Aral Sea region and the Aral Sea, a forecast of its transformation by 2020 year is developed. We develop a practical plan of action for cardinal improvement of the environment in the Amu Darya Delta and the dried bottom of the Aral Sea.

  6. FORECASTING OF ECONOMIC GROWTH OF REGION IN CONDITIONS OF DEFICIENCY OF THE INFORMATION

    Directory of Open Access Journals (Sweden)

    S.L. Sadov

    2007-12-01

    Full Text Available The new approach to forecasting economic growth of the region, showing minimal requirements to a supply with information, is offered in clause. It is based on combinatory likelihood modelling of dependence of a parameter of economic growth from its reliability. The method we shall apply to regions with the expressed branch specialization for the period before realization of structural reorganization of economy. In the conclusion the forecast of growth of Republic Komi VRP up to 2020 is given − is shown, that it will make 4 − 6% a year at preservation of energy raw specializations.

  7. Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

    2010-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

  8. 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.

  9. Skills of different mesoscale models over Indian region during monsoon season: Forecast errors

    Indian Academy of Sciences (India)

    Someshwar Das; Raghavendra Ashrit; Gopal Raman Iyengar; Saji Mohandas; M Das Gupta; John P George; E N Rajagopal; Surya Kanti Dutta

    2008-10-01

    Performance of four mesoscale models namely,the MM5,ETA,RSM and WRF,run at NCMRWF for short range weather forecasting has been examined during monsoon-2006.Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind,temperature,specific humidity,geopotential height,rainfall,systematic errors,root mean square errors and specific events like the monsoon depressions. It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none ’.Perhaps an ensemble approach would be the best.However, if we must make a final verdict,it can be stated that in general,(i)the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and,the MM5 is able to produce best All India rainfall forecasts in day-3,but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India,(ii)the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time,and (iii)the RSM is able to produce least errors in the day-1 forecasts of the tracks,while the ETA model produces least errors in the day-3 forecasts.

  10. Validating quantitative precipitation forecast for the Flood Meteorological Office, Patna region during 2011-2014

    Science.gov (United States)

    Giri, R. K.; Panda, Jagabandhu; Rath, Sudhansu S.; Kumar, Ravindra

    2016-06-01

    In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitation is required. In view of this, the present study intends to validate the quantitative precipitation forecast (QPF) issued during southwest monsoon season for six river catchments (basin) under the flood meteorological office, Patna region. The forecast is analysed statistically by computing various skill scores of six different precipitation ranges during the years 2011-2014. The analysis of QPF validation indicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitation ranges of 1-10 and 11-25 mm. However, the reliability decreases for higher ranges of rainfall and also for the lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecasting for QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It is realized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively useful for issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However, QPF may be improved using satellite and radar products.

  11. Developing and testing solar irradiance forecasting techniques in the Hawaiian Islands region

    Science.gov (United States)

    Matthews, D. K.

    2015-12-01

    The Hawaíi Natural Energy Institute (HNEI) is developing an operational solar forecasting for the Hawaiian Islands. The system comprises the following three components, covering forecasting horizons from seconds to days ahead. (i) A ground-observation driven advection model, using sky imagery and cloud height data. (ii) A satellite-image based advection model, primarily driven by Geostationary Operational Environmental Satellite (GOES) imagery. (iii) A coupled ocean-atmosphere model, using the Regional Ocean Modeling System (ROMS) model and the Weather Research and Forecasting (WRF) model, including newly available microphysics, shallow convection parameterization, and radiative transfer physics options. The satellite and NWP components provide coverage for the entire island chain, however, lack the resolution in time and space, to accurately forecast ramp events (large changes in irradiance that occur over a short period of time). Knowledge of the magnitude, duration and timing of ramp events are particularly important in Hawaíi due to the small size of the electric grids. Currently, HNEI employs a sky imager and ceilometer installed on the University of Hawaíi campus for high resolution forecasting, however, instrument design and cost limit widespread deployment. We discuss the development and preliminary validation of a new forecasting system based on inexpensive, panoramic (large FOV), off-the-shelf cameras with a cloud base height retrieval algorithm that does not require additional instrumentation.

  12. Regional landslide forecasting model using interferometric SAR images

    Institute of Scientific and Technical Information of China (English)

    董育烦; 张发明; 高正夏; 蒯志要

    2008-01-01

    Method of obtaining landslide evaluating information by using Interferometric Synthetic Aperture Radar (InSAR) technique was discussed. More precision landslide surface deformation data extracted from InSAR image need take suitable SAR interferometric data selecting, path tracking, phase unwrapping processes. Then, the DEM model of scope and surface shape of the landslide was built. Combining with geological property of landslide and sliding displacements obtained from InSAR/D-InSAR images, a new landslide forecasting model called equal central angle slice method for those not obviously deformed landslides was put forward. This model breaks the limits of traditional research methods of geology. In this model, the landslide safety factor was calculated by equal central angle slice method, then considering the persistence ratio of the sliding surface based on plastic theory, the minimum safety factor was the phase when plastic area were complete persistence. This new model makes the application of InSAR/D-InSAR technology become more practical in geology hazard research.

  13. An analysis of seasonal forecasts from POAMA and SCOPIC in the Pacific region

    Science.gov (United States)

    Cottrill, Andrew; Charles, Andrew; Kuleshov, Yuriy

    2013-04-01

    The Australian Bureau of Meteorology (BoM), as part of the Pacific Island Climate Prediction Project (PI-CPP), has developed seasonal forecasts for ten National Meteorological Services (NMS) in the Pacific region for nearly a decade, to improve seasonal forecast services to local communities and industry. As part of this project, a new statistical model called SCOPIC (Seasonal Climate Outlooks for Pacific Island Countries) was developed to provide partner countries with the ability to produce their own seasonal climate outlooks. In 2010, as part of the Pacific Adaptation Strategy and Assistance Programme (PASAP), the BoM developed a seasonal outlook portal for Pacific NMS as an alternative source of seasonal forecasts based on the Bureau's dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia). This dynamical model is a coupled ocean-atmosphere model, which has been developed by the Bureau for over ten years for forecasting research in Australia. However, no formal assessment of the skill of the two forecast systems (POAMA and SCOPIC) has been carried out using a number of skill metrics for the Pacific region. Although the skill of POAMA in the Australian region is now well documented, the forecast skill is even higher in the Pacific region due to its proximity to the tropical ocean, where the El Niño-Southern Oscillation (ENSO) provides the main source of tropical climate variability and predictability on seasonal time scales. The statistical model (SCOPIC) uses discriminant analysis (multiple linear regression) and the relationships of sea surface temperatures (SST) or the Southern Oscillation Index (predictors) and monthly rainfall (predictands) to predict rainfall at various lead times. In contrast, POAMA uses the current state of the climate (initial ocean and atmospheric conditions) and model physics to predict forecasts of many climate variables at all locations across the globe and also at various lead times. Here we demonstrate the skill

  14. 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).

  15. 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.

  16. Preferences of customers from Podkarpacie concerning demand on regional products

    Directory of Open Access Journals (Sweden)

    Maria Grzybek

    2009-01-01

    Full Text Available The paper includes an analysis of the results of the research conducted among 600 consumers from six communes of Podkarpackie voivodship. The research proved, that customers buying natural regional products prefer, to a large extent, their quality, wholesomeness, price, producer’s brand and packaging. It should be stressed, that price, which was previously in the first place, in the opinion of the surveyed population takes the third position, and the first one is taken by quality. The research showed the differences between men and women in preference of features of regional products. The majority of women paid attention to quality, price and brand, men, on the other hand, to wholesomeness and product’s packaging. High quality and price were in the scope of interest of the youngest and retired people. Older people were more interested in brand and packaging than younger ones. Quality of regional goods was preferred regardless of the number of family members Brand and packaging were the most important features representing one-person households. The richest consumers preferred quality, price, brand, packaging, while the poorest ones paid attention to wholesomeness, quality and price.

  17. Forecasting Water Demand in Residential, Commercial, and Industrial Zones in Bogotá, Colombia, Using Least-Squares Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Carlos Peña-Guzmán

    2016-01-01

    Full Text Available The Colombian capital, Bogotá, has undergone massive growth in a short period of time. Naturally, this growth has increased the city’s water demand. The prediction of this demand will help understand and analyze consumption behavior, thereby allowing for effective management of the urban water cycle. This paper uses the Least-Squares Support Vector Machines (LS-SVM model for forecasting residential, industrial, and commercial water demand in the city of Bogotá. The parameters involved in this study include the following: monthly water demand, number of users, and total water consumption bills (price for the three studied uses. Results provide evidence of the model’s accuracy, producing R2 between 0.8 and 0.98, with an error percentage under 12%.

  18. Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

    Science.gov (United States)

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

    2010-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

  19. 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.

  20. Forecast Analysis on Quantity of Demand for Raw Materials of Wood Processing Factory%木材加工厂原料需求量预测分析

    Institute of Scientific and Technical Information of China (English)

    邓蓉; 邱荣祖

    2014-01-01

    Forecast the demand for wood raw material quantity is a key link of wood processing enterprises inventory control,even may directly affect overall planning for future operations planning of the plant.In this paper a man-made board factory of Fujian,which's wood raw material demand was for basic date,and the combination forecasting model using grey prediction and three exponential smoothing prediction was established,also the error sum of squares minimization method was adopted to define the optimal weights of single prediction model,finally the demand for wood raw material of man-made board factory was forecasted and analysed.%木质原料需求量预测是木材加工企业库存控制的关键环节,甚至会直接影响到加工厂未来运营计划的整体规划。文中以福建某造板厂木材原料需求量为基础数据,建立组合灰色预测和三次指数平滑预测的预测模型,采用误差平方和极小化的方法确定单项预测模型最优权重,并对造板厂木材原料需求量加以预测和分析。

  1. Forecasting electricity demand based on data mining%基于数据挖掘的电力需求预测探究

    Institute of Scientific and Technical Information of China (English)

    李其军

    2015-01-01

    电力需求的预测是电力服务企业制定供电、购电的重要依据,因此,做好对电力需求的预测,对提高电力企业的经济运行能力具有重要的作用。本文结合原始预测系统中存在的问题,提出采用数据挖掘技术对原始数据的采集、预处理等,从而实现对电力需求预测的客观性和准确性,更好的服务与电力企业和社会。%Electricity demand forecasting electricity supply service companies develop,purchase an important basis for electricity and, therefore, do a good job of forecasting electricity demand,the ability to improve the economic operation of power enterprises play an important role.In this paper,the original prediction system problems using data mining techniques proposed acquisition of the raw data,pre-processing , etc.,in order to achieve objectivity and accuracy of forecasts of electricity demand,better service and electricity business and society .

  2. The CYCOFOS new forecasting systems at regional and sub-regional scales for supporting the marine safety

    Science.gov (United States)

    Zodiatis, George; Radhakrishnan, Hari; Galanis, George; Nikolaidis, Andreas; Emmanouil, George; Nikolaidis, Georgios; Lardner, Robin; Sofianos, Sarantis; Stylianou, Stavros; Nikolaidis, Marios

    2016-04-01

    The CYCOFOS new forecasting systems at regional and sub-regional scales for supporting the marine safety George Zodiatis1, Hari Radhakrishnan1, George Galanis1,2, Andreas Nikolaidis1, George Emmanouil1,2, Georgios Nikolaidis1, Robin Lardner1, Sarantis Sofianos3, Stavros Stylianou1 and Marios Nikolaidis1 1Oceanography Centre, University of Cyprus, Nicosia 1678, Cyprus 2 Hellenic Naval Academy, Section of Mathematics, Piraeus 18539, Greece 3 University of Athens, Ocean Physics and Modeling Group, Athens 15784, Greece The Cyprus Coastal Ocean FOrecasting System-CYCOFOS has been providing operational hydrodynamic and sea state forecasts in the Eastern Mediterranean since early 2002. Recently, it has been improved with the implementation of new hydrodynamic, wave and atmospheric models, targeting larger and higher resolution domains at regional and sub-regional scales. For the new CYCOFOS hydrodynamic system a novel parallel version of POM has been implemented. The new flow model covers the Eastern Mediterranean with a resolution of 2 km and the Levantine with 500 m, both nested in Copernicus Marine Environmental Monitoring Service-CMEMS. The CYCOFOS hydrodynamic model is coupled with the latest ECMWF WAM model. The surface currents produced from the Copernicus marine service and CYCOFOS has been incorporated in the wave integration, providing a second independent forcing input to the new CYCOFOS wave model, in addition to the winds. The Weather Research and Forecasting atmospheric model-WRF has been implemented in the same domain as SKIRON atmospheric model, in order to provide the backup forcing for the CYCOFOS models. The improved CYCOFOS forecasting data are used for the EU CISE 2020 project to establish an ΕU Common Information Sharing Environment to improve the Maritime Situational Awareness, particularly for SAR operations, as well as for the MEDESS4MS multi model oil spill prediction service, for operational oil spill predictions in the Mediterranean.

  3. PI forecast with or without de-clustering: an experiment for the Sichuan-Yunnan region

    Directory of Open Access Journals (Sweden)

    C. S. Jiang

    2011-03-01

    Full Text Available Pattern Informatics (PI algorithm uses earthquake catalogues for estimating the increase of the probability of strong earthquakes. The main measure in the algorithm is the number of earthquakes above a threshold magnitude. Since aftershocks occupy a significant proportion of the total number of earthquakes, whether de-clustering affects the performance of the forecast is one of the concerns in the application of this algorithm. This problem is of special interest after a great earthquake, when aftershocks become predominant in regional seismic activity. To investigate this problem, the PI forecasts are systematically analyzed for the Sichuan-Yunnan region of southwest China. In this region there have occurred some earthquakes larger than MS 7.0, including the 2008 Wenchuan earthquake. In the analysis, the epidemic-type aftershock sequences (ETAS model was used for de-clustering. The PI algorithm was revised to consider de-clustering, by replacing the number of earthquakes by the sum of the ETAS-assessed probability for an event to be a "background event" or a "clustering event". Case studies indicate that when an intense aftershock sequence is included in the "sliding time window", the hotspot picture may vary, and the variation lasts for about one year. PI forecasts seem to be affected by the aftershock sequence included in the "anomaly identifying window", and the PI forecast using "background events" seems to have a better performance.

  4. Impact of various observing systems on weather analysis and forecast over the Indian region

    Science.gov (United States)

    Singh, Randhir; Ojha, Satya P.; Kishtawal, C. M.; Pal, P. K.

    2014-09-01

    To investigate the potential impact of various types of data on weather forecast over the Indian region, a set of data-denial experiments spanning the entire month of July 2012 is executed using the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system. The experiments are designed to allow the assessment of mass versus wind observations and terrestrial versus space-based instruments, to evaluate the relative importance of the classes of conventional instrument such as radiosonde, and finally to investigate the role of individual spaceborne instruments. The moist total energy norm is used for validation and forecast skill assessment. The results show that the contribution of wind observations toward error reduction is larger than mass observations in the short range (48 h) forecast. Terrestrial-based observations generally contribute more than space-based observations except for the moisture fields, where the role of the space-based instruments becomes more prevalent. Only about 50% of individual instruments are found to be beneficial in this experiment configuration, with the most important role played by radiosondes. Thereafter, Meteosat Atmospheric Motion Vectors (AMVs) (only for short range forecast) and Special Sensor Microwave Imager (SSM/I) are second and third, followed by surface observations, Sounder for Probing Vertical Profiles of Humidity (SAPHIR) radiances and pilot observations. Results of the additional experiments of comparative performance of SSM/I total precipitable water (TPW), Microwave Humidity Sounder (MHS), and SAPHIR radiances indicate that SSM/I is the most important instrument followed by SAPHIR and MHS for improving the quality of the forecast over the Indian region. Further, the impact of single SAPHIR instrument (onboard Megha-Tropiques) is significantly larger compared to three MHS instruments (onboard NOAA-18/19 and MetOp-A).

  5. Forecast Verification for North American Mesoscale (NAM) Operational Model over Karst/Non-Karst regions

    Science.gov (United States)

    Sullivan, Z.; Fan, X.

    2014-12-01

    Karst is defined as a landscape that contains especially soluble rocks such as limestone, gypsum, and marble in which caves, underground water systems, over-time sinkholes, vertical shafts, and subterranean river systems form. The cavities and voids within a karst system affect the hydrology of the region and, consequently, can affect the moisture and energy budget at surface, the planetary boundary layer development, convection, and precipitation. Carbonate karst landscapes comprise about 40% of land areas over the continental U.S east of Tulsa, Oklahoma. Currently, due to the lack of knowledge of the effects karst has on the atmosphere, no existing weather model has the capability to represent karst landscapes and to simulate its impact. One way to check the impact of a karst region on the atmosphere is to check the performance of existing weather models over karst and non-karst regions. The North American Mesoscale (NAM) operational forecast is the best example, of which historical forecasts were archived. Variables such as precipitation, maximum/minimum temperature, dew point, evapotranspiration, and surface winds were taken into account when checking the model performance over karst versus non-karst regions. The forecast verification focused on a five-year period from 2007-2011. Surface station observations, gridded observational dataset, and North American Regional Reanalysis (for certain variables with insufficient observations) were used. Thirteen regions of differing climate, size, and landscape compositions were chosen across the Contiguous United States (CONUS) for the investigation. Equitable threat score (ETS), frequency bias (fBias), and root-mean-square error (RMSE) scores were calculated and analyzed for precipitation. RMSE and mean bias (Bias) were analyzed for other variables. ETS, fBias, and RMSE scores show generally a pattern of lower forecast skills, a greater magnitude of error, and a greater under prediction of precipitation over karst than

  6. 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.

  7. 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.

  8. 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

  9. Forecast of biogas generation in Lithuanian regional land fills

    Directory of Open Access Journals (Sweden)

    Brigita Šalčiūnaitė

    2015-10-01

    Full Text Available The amount of generated waste in Lithuania ranges from 35,000 tons to 261,865 tons per year. 35% of this quantity is biodegradable waste – i.e. about 27,830 tons/year. 75% of municipal waste in Lithuania is disposed in landfills. Such management of municipal waste is dangerous because of environmental pollution with the biogas and leachate, increased global greenhouse effect, and so on. From 1 ton of landfill waste it is possible to get about 10 m3 of landfill gas, after using it 13 kWh and 50 kWh of thermal energy could be made. There was presented in this article the estimated amount of biogas produced by Lithuanian regional landfills each separately and all co-production of biogas from landfills potential Lithuania, using the software LandGEM. Total volume of gas liberated in landfills ranges from 7.8 × 106 to 5.8 × 107 m3/year of methane and carbon dioxide in an amount ranging from 3.9 × 106 to 2.9 × 107 m3/year. Minimum quantities of biogas generated in Tauragės regional landfill – 7.8 × 106 m3/year, and the highest – Kaunas regional landfill – 5.8 × 107 m3/year. From the generated biogass it would be possible to yield 2,145 × 108 kWh of electricity and 8.25 × 108 kWh of thermal energy.

  10. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

  11. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    Wang Ting

    2009-01-01

    @@ The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

  12. FARMERS’ WILLINGNESS TO PAY FOR WEATHER FORECAST INFORMATION IN SAVELUGU-NANTON MUNICIPALITY OF THE NORTHERN REGION

    OpenAIRE

    Franklin, Nantui; Prince, Nketiah; Daniel, Darko

    2014-01-01

    In the quest for farmers to get maximum output and improve their livelihood within the major constraint of depending on rain-fed agriculture, the issue of having access to weather forecast information is very important. A contingent valuation method was used to elicit the amount farmers are willing to pay for accessing unpriced weather forecast information in the Savelugu-Nanton Municipality of the Northern Region. Farmers were also asked to rank weather forecast variables according to their ...

  13. Predictability and uncertainty of the GloFAS forecasts in the Pacific region of Peru

    Science.gov (United States)

    Boelee, Leonore; Samuals, Paul; Lumbroso, Darren; Zsoter, Ervin; Stephens, Elisabeth; Cloke, Hannah; Baso, Juan

    2017-04-01

    GloFAS is a global flood awareness system based on a distributed hydrological model forced with numerical ensemble weather predictions (Alfieri et al. 2013). Results are published on a password-protected website. Forecasts from the GloFAS are currently limited in resolution and quality, but are nonetheless being used by humanitarian and aid organisations and a small number of forecasting agencies. One such agency is SENHAMI in Peru. To get around the limited accuracy issue, SENHAMI are applying a simple bias correction to the initial conditions of the GloFAS forecasts. This process is reliant on in situ measurements being available and reliable, therefore limiting the locations which can be corrected. Also, the uncertainties of the initials conditions are reduced but the remaining uncertainties will continue limiting the predictability of the forecasts. This research aims to understand and quantify the inaccuracy and uncertainties in the GloFAS forecasts for the Pacific region of Peru. The work will explore ways of improving the predictability of the forecasts within the GloFAS framework. The research will start with looking at the performance of the three main components of the GloFAS forecasting system: the forcing data, the runoff component and the flow routing component. The forcing data, consisting of the ERA-Intrim (Dee et al. 2011) and Variable Resolution Ensemble Prediction System (Miller et al. 2010),will be validated. The starting point will be finding if the weak rainfall along the Pacific coast caused by the large-scale mid tropospheric subsidence over the southeaster subtropical Pacific Ocean and enhanced by the coastal upwelling of cold air (Garreaud, Rutliant, and Fuenzalida 2002), is present in the forcing data. The representation of the hydrological processes, as done by HTESSEL, will be analysed focussing on the surface runoff, subsurface runoff and soil moisture. The results of the flow routing model, LisFlood-Global, will be validated, focussing

  14. CFORS - Regional Chemical and Weather Forecast System in Support of Field Experiments

    Science.gov (United States)

    Yienger, J. J.; Uno, I.; Guttikunda, S. K.; Carmichael, G. R.; Tang, Y.; Thongboonchoo, N.; Woo, J.; Dorwart, J.; Streets, D.

    2001-12-01

    In this paper we will present the development, evaluation, and use of improved modeling techniques and methodologies for the integration of meteorological forecasts with air pollution forecasts in support of field operations during the TRACE-P and Ace-Asia experiments in East Asia. During the campaign period we provided a variety of forecast products using our regional modeling system built upon the dynamic meteorological model RAMS and the 3-D regional chemical transport models STEM-III. These models were run in both on-line and off-line modes, and the results integrated into an interactive web-based data mining and analysis framework. This resulting Chemical Weather Forecasting System CFORS, was run operationally for the period February through May 2001, and provided 72-hr forecasts of a variety of aerosol, chemical and air mass and emission marker quantities. These included aerosol mass distribution and optical depth by major component (e.g., dust, sea salt, black carbon, organic carbon, and sulfate), photochemical quantities including ozone and OH/HO2, and air mass & emissions markers including lightning, volcanic, mega-cities, and biomass burning. These model products were presented along with meteorological forecasts and satellite products, and used to help determine the flight plans, the positioning of the ship, and to alert surface stations of upcoming events (such as dust storms). The use of CFORS forecasts (along with other model results) models were shown to provide important new information and level of detail into mission planning. For example many of the mission objectives required designing flight paths that sampled across gradients of optical depth, or flew above, below and through vertical layers of aerosol, intercepted biomass emission plumes, or sampled dust storms. CFORS, forecasts of dust outbreaks and plume locations, etc., proved to be very useful in designing missions that meet these objective. In this paper we will present an overview of

  15. 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.

  16. 企业需求预测准确性存在的问题及其应对策略(二)%The enterprise demand forecast accuracy problems and coping strategiesⅡ

    Institute of Scientific and Technical Information of China (English)

    董鹏; 赵得龙; 杨阿南

    2014-01-01

    鉴于需求预测在企业经营活动中具有重要地位,且会受到受各种因素影响,本文在对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析的基础上,指出了通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和未来市场情况的偏差,从而持续提高需求预测的准确性,促进企业生产、销售的良性运行。%In view of the demand forecast diversity plays an important role in the business activities of enterprises and its influence factors, by combing and analyzing method, to predict the actual demand of enterprises and the process, system, management and so on, pointed out that improving demand forecasting system, improving demand forecasting management through optimizing the demand forecasting method, can be effective, deviation control demand forecasting and future market conditions, so as to continuously improve the accuracy of demand forecasting, promote the healthy operation of enterprise production, sales.

  17. Analysis and Evaluation of Forecasting Methods and Tools to Predict Future Demand for Secondary Chemical-Biological Configuration Items

    Science.gov (United States)

    2013-06-01

    Deviation MAPE Mean Absolute Percentage Error MOA Memorandum of Agreement MRP Material Requirements Planning MSD Mean Squared Deviation MSE Mean...absolute percentage error ( MAPE ) for each forecast, given by the following equations (10) (11) and (12): where : ei = forecast error...1-1047 987654321 2500 2250 2000 1750 1500 Index 5- 1- 10 47 Q tr D em an d MAPE 14.4 MAD 286.2 MSD 97841.1 Accuracy Measures Actual Fits Variable

  18. Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

    Science.gov (United States)

    Kirschbaum, Dalia B.; Adler, Robert; Hone, Yang; Kumar, Sujay; Peters-Lidard, Christa; Lerner-Lam, Arthur

    2010-01-01

    A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve

  19. Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

    Science.gov (United States)

    Kirschbaum, Dalia B.; Adler, Robert; Hone, Yang; Kumar, Sujay; Peters-Lidard, Christa; Lerner-Lam, Arthur

    2010-01-01

    A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve

  20. Exploiting teleconnection indices for probabilistic forecasting of drought class transitions in Sicily region (Italy)

    Science.gov (United States)

    Bonaccorso, Brunella; Cancelliere, Antonino

    2015-04-01

    In the present study two probabilistic models for short-medium term drought forecasting able to include information provided by teleconnection indices are proposed and applied to Sicily region (Italy). Drought conditions are expressed in terms of the Standardized Precipitation-Evapotranspiration Index (SPEI) at different aggregation time scales. More specifically, a multivariate approach based on normal distribution is developed in order to estimate: 1) on the one hand transition probabilities to future SPEI drought classes and 2) on the other hand, SPEI forecasts at a generic time horizon M, as functions of past values of SPEI and the selected teleconnection index. To this end, SPEI series at 3, 4 and 6 aggregation time scales for Sicily region are extracted from the Global SPEI database, SPEIbase , available at Web repository of the Spanish National Research Council (http://sac.csic.es/spei/database.html), and averaged over the study area. In particular, SPEIbase v2.3 with spatial resolution of 0.5° lat/lon and temporal coverage between January 1901 and December 2013 is used. A preliminary correlation analysis is carried out to investigate the link between the drought index and different teleconnection patterns, namely: the North Atlantic Oscillation (NAO), the Scandinavian (SCA) and the East Atlantic-West Russia (EA-WR) patterns. Results of such analysis indicate a strongest influence of NAO on drought conditions in Sicily with respect to other teleconnection indices. Then, the proposed forecasting methodology is applied and the skill in forecasting of the proposed models is quantitatively assessed through the application of a simple score approach and of performance indices. Results indicate that inclusion of NAO index generally enhance model performance thus confirming the suitability of the models for short- medium term forecast of drought conditions.

  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. Validating quantitative precipitation forecast for the Flood Meteorological Office, Patna region during 2011–2014

    Indian Academy of Sciences (India)

    R K Giri; Jagabandhu Panda; Sudhansu S Rath; Ravindra Kumar

    2016-06-01

    In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitationis required. In view of this, the present study intends to validate the quantitative precipitationforecast (QPF) issued during southwest monsoon season for six river catchments (basin) under theflood meteorological office, Patna region. The forecast is analysed statistically by computing various skillscores of six different precipitation ranges during the years 2011–2014. The analysis of QPF validationindicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitationranges of 1–10 and 11–25 mm. However, the reliability decreases for higher ranges of rainfall and also forthe lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecastingfor QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It isrealized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively usefulfor issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However,QPF may be improved using satellite and radar products.

  3. Study on Forecasting of Agricultural Produce Logistics Demand of Xinjiang Construction Corps%兵团农产品物流需求预测研究

    Institute of Scientific and Technical Information of China (English)

    成观雄; 朱叶; 喻晓玲

    2014-01-01

    探讨了兵团农产品物流发展现状、存在问题及其制约因素。运用灰色模型和神经网络两种方法对兵团农产品物流需求进行预测,并进行了组合预测,具有一定的实际意义。%In this paper, we discussed the current status, existing problems and restricting factors of the agricultural produce logistics of the Xinjiang Construction Corps, then used the grey model and neural network to forecast the demand for agricultural produce logistics by the Corps, and at the end, performed a combination forecasting which was of certain practical significance.

  4. 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

  5. Northeast Coastal Ocean Forecast System (NECOFS): A Multi-scale Global-Regional-Estuarine FVCOM Model

    Science.gov (United States)

    Beardsley, R. C.; Chen, C.

    2014-12-01

    The Northeast Coastal Ocean Forecast System (NECOFS) is a global-regional-estuarine integrated atmosphere/surface wave/ocean forecast model system designed for the northeast US coastal region covering a computational domain from central New Jersey to the eastern end of the Scotian Shelf. The present system includes 1) the mesoscale meteorological model WRF (Weather Research and Forecasting); 2) the regional-domain FVCOM covering the Gulf of Maine/Georges Bank/New England Shelf region (GOM-FVCOM); 3) the unstructured-grid surface wave model (FVCOM-SWAVE) modified from SWAN with the same domain as GOM-FVCOM; 3) the Mass coastal FVCOM with inclusion of inlets, estuaries and intertidal wetlands; and 4) three subdomain wave-current coupled inundation FVCOM systems in Scituate, MA, Hampton River, NH and Mass Bay, MA. GOM-FVCOM grid features unstructured triangular meshes with horizontal resolution of ~ 0.3-25 km and a hybrid terrain-following vertical coordinate with a total of 45 layers. The Mass coastal FVCOM grid is configured with triangular meshes with horizontal resolution up to ~10 m, and 10 layers in the vertical. Scituate, Hampton River and Mass Bay inundation model grids include both water and land with horizontal resolution up to ~5-10 m and 10 vertical layers. GOM-FVCOM is driven by surface forcing from WRF model output configured for the region (with 9-km resolution), the COARE3 bulk air-sea flux algorithm, local river discharges, and tidal forcing constructed by eight constituents and subtidal forcing on the boundary nested to the Global-FVCOM. SWAVE is driven by the same WRF wind field with wave forcing at the boundary nested to Wave Watch III configured for the northwestern Atlantic region. The Mass coastal FVCOM and three inundation models are connected with GOM-FVCOM through one-way nesting in the common boundary zones. The Mass coastal FVCOM is driven by the same surface forcing as GOM-FVCOM. The nesting boundary conditions for the inundation models

  6. Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Department

    Science.gov (United States)

    Case. Jonathan; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

    2014-01-01

    Flooding and drought are two key forecasting challenges for the Kenya Meteorological Department (KMD). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the boundary layer of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-end events over east Africa. KMD currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Nonhydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over eastern Africa. Two organizations at the National Aeronautics and Space Administration Marshall Space Flight Center in Huntsville, AL, SERVIR and the Short-term Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMD for enhancing its regional modeling capabilities. To accomplish this goal, SPoRT and SERVIR will provide experimental land surface initialization datasets and model verification capabilities to KMD. To produce a land-surface initialization more consistent with the resolution of the KMD-WRF runs, the NASA Land Information System (LIS

  7. Elasticity of Export Demand for Australian Sugar: Accounting for Regional and Seasonal Effects

    OpenAIRE

    Longmire, James L.; Males, Warren P.

    1997-01-01

    The elasticity of export demand for Australian sugar is an important measure for devising sugar export marketing strategies and considering the impact of various policies on the industry. Updated and more explicit elasticities of export demand for Australian sugar are reported in this paper. The elasticities are calculated using an adaptation of the formula approach published by Cronin (1979). Initially elasticities are reported for Australia without accounting for regional and seasonal effec...

  8. Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

    Institute of Scientific and Technical Information of China (English)

    XU Jianming; HE Jinhai; YANG Yuanqin; WANG Jiahe; XU Xiangde; LIU Yu; DING Guoan; CHEN Huailiang; HU Jiangkai; ZHANG Jianchun; WU Hao; LI Weiliang

    2005-01-01

    Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution

  9. Calibration of seasonal forecasts over Euro-Mediterranean region: improve climate information for the applications in the energy sector

    Science.gov (United States)

    De Felice, Matteo; Alessandri, Andrea; Catalano, Franco

    2013-04-01

    Accurate and reliable climate information, calibrated for the specific geographic domain, are critical for an effective planning of operations in industrial sectors, and more in general, for all the human activities. The connection between climate and energy sector became particularly evident in the last decade, due to the diffusion of renewable energy sources and the consequent attention on the socio-economical effects of extreme climate events .The energy sector needs reliable climate information in order to plan effectively power plants operations and forecast energy demand and renewable output. On time-scales longer than two weeks (seasonal), it is of critical importance the optimization of global climate information on the local domains needed by specific applications. An application that is distinctly linked with climate is electricity demand forecast, in fact, especially during cold/hot periods, the electricity usage patterns are influenced by the use of electric heating/cooling equipments which diffusion is steadily increasing worldwide [McNeil & Letschert, 2007]. Following an approach similar to [Navarra & Tribbia, 2005], we find a linear relationship between seasonal forecasts main modes of temperature anomaly and the main modes of reanalysis on Euro-Mediterranean domain. Then, seasonal forecasts are calibrated by means of a cross-validation procedure with the aim of optimize climate information over Italy. Calibrated seasonal forecasts are used as predictor for electricity demand forecast on Italy during the summer (JJA) in the period 1990-2009. Finally, a comparison with the results obtained with not calibrated climate forecasts is performed. The proposed calibration procedure led to an improvements of electricity demand forecast performance with more evident effects on the North of Italy, reducing the overall RMSE of 10% (from 1.09 to 0.98). Furthermore, main principal components are visualized and put in relation with electricity demand patterns in

  10. Analysis of Logistics Demand Based on Gray Forecasting Model%基于灰色预测模型的物流需求分析

    Institute of Scientific and Technical Information of China (English)

    刘源

    2012-01-01

    以灰色理论和灰色预测模型中的GM(1,1)模型的原理与方法为基础,运用GM(1,1)模型和MATLAB程序,以河南省为例,对物流需求进行分析预测.%Based on the gray theory and the principle and method of the GM(1,1) model, we have used the GM(1,1) model and the program MATI,AB to forecast, in (he case of Henan province, the demand for the logistics industry.

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-03 (NODC Accession 0001531)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-12 (NODC Accession 0002659)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-06 (NODC Accession 0002406)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-07 (NODC Accession 0001523)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-09 (NODC Accession 0001525)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-04 (NODC Accession 0001520)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-06 (NODC Accession 0001522)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-05 (NODC Accession 0001533)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-03 (NODC Accession 0001519)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-05 (NODC Accession 0001545)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-10 (NODC Accession 0043271)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-03 (NODC Accession 0002742)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-04 (NODC Accession 0001532)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-11 (NODC Accession 0001527)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-09 (NODC Accession 0043270)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-10 (NODC Accession 0001550)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-12 (NODC Accession 0043273)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-02 (NODC Accession 0001554)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-09 (NODC Accession 0001549)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-02 (NODC Accession 0001542)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-02 (NODC Accession 0001530)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-11 (NODC Accession 0001587)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-05 (NODC Accession 0001569)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-08 (NODC Accession 0001524)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-01 (NODC Accession 0001529)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-03 (NODC Accession 0001603)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-05 (NODC Accession 0043281)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-07 (NODC Accession 0001571)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-05 (NODC Accession 0001521)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-04 (NODC Accession 0001568)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-07 (NODC Accession 0001559)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-03 (NODC Accession 0001591)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-08 (NODC Accession 0043284)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-10 (NODC Accession 0001562)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-01 (NODC Accession 0001517)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-03 (NODC Accession 0002162)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-04 (NODC Accession 0043262)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-01 (NODC Accession 0001577)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-12 (NODC Accession 0001588)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-09 (NODC Accession 0001573)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-01 (NODC Accession 0001565)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-08 (NODC Accession 0002504)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-02 (NODC Accession 0001590)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-06 (NODC Accession 0043282)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-07 (NODC Accession 0001583)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-08 (NODC Accession 0001560)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-08 (NODC Accession 0043268)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-09 (NODC Accession 0001585)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-06 (NODC Accession 0001558)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-07 (NODC Accession 0043267)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-03 (NODC Accession 0001579)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-06 (NODC Accession 0001582)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-03 (NODC Accession 0001567)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-11 (NODC Accession 0001551)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-01 (NODC Accession 0002660)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-02 (NODC Accession 0001518)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-12 (NODC Accession 0001576)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-08 (NODC Accession 0001596)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-12 (NODC Accession 0001564)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-09 (NODC Accession 0001597)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-04 (NODC Accession 0001556)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-04 (NODC Accession 0001604)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-02 (NODC Accession 0001566)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-07 (NODC Accession 0043283)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-05 (NODC Accession 0001593)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-11 (NODC Accession 0001599)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-06 (NODC Accession 0001594)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-12 (NODC Accession 0001600)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-08 (NODC Accession 0001584)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-04 (NODC Accession 0002340)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-09 (NODC Accession 0001561)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-04 (NODC Accession 0001592)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-02 (NODC Accession 0002160)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-08 (NODC Accession 0001572)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-01 (NODC Accession 0002159)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-01 (NODC Accession 0001601)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-11 (NODC Accession 0001575)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-11 (NODC Accession 0001563)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-10 (NODC Accession 0001598)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-05 (NODC Accession 0043265)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-06 (NODC Accession 0001570)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-07 (NODC Accession 0001595)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-07 (NODC Accession 0001535)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-12 (NODC Accession 0001528)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-05 (NODC Accession 0001581)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-08 (NODC Accession 0002154)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-05 (NODC Accession 0002373)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-02 (NODC Accession 0001578)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-01 (NODC Accession 0001589)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-06 (NODC Accession 0002151)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-06 (NODC Accession 0043266)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-09 (NODC Accession 0002505)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-10 (NODC Accession 0001526)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-06 (NODC Accession 0001534)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-08 (NODC Accession 0001548)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-04 (NODC Accession 0001544)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-07 (NODC Accession 0001547)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-02 (NODC Accession 0001602)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-10 (NODC Accession 0001586)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-01 (NODC Accession 0001541)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-12 (NODC Accession 0001540)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-04 (NODC Accession 0001580)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-06 (NODC Accession 0001546)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-03 (NODC Accession 0001543)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-09 (NODC Accession 0001537)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-10 (NODC Accession 0002156)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-11 (NODC Accession 0002652)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. 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.

  19. Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation

    Science.gov (United States)

    Ehsan, Muhammad Azhar; Tippett, Michael K.; Almazroui, Mansour; Ismail, Muhammad; Yousef, Ahmed; Kucharski, Fred; Omar, Mohamed; Hussein, Mahmoud; Alkhalaf, Abdulrahman A.

    2016-07-01

    Northern Hemisphere winter precipitation reforecasts from the European Centre for Medium Range Weather Forecast System-4 and six of the models in the North American Multi-Model Ensemble are evaluated, focusing on two regions (Region-A: 20°N-45°N, 10°E-65°E and Region-B: 20°N-55°N, 205°E-255°E) where winter precipitation is a dominant fraction of the annual total and where precipitation from mid-latitude storms is important. Predictability and skill (deterministic and probabilistic) are assessed for 1983-2013 by the multimodel composite (MME) of seven prediction models. The MME climatological mean and variability over the two regions is comparable to observation with some regional differences. The statistically significant decreasing trend observed in Region-B precipitation is captured well by the MME and most of the individual models. El Niño Southern Oscillation is a source of forecast skill, and the correlation coefficient between the Niño3.4 index and precipitation over region A and B is 0.46 and 0.35, statistically significant at the 95 % level. The MME reforecasts weakly reproduce the observed teleconnection. Signal, noise and signal to noise ratio analysis show that the signal variance over two regions is very small as compared to noise variance which tends to reduce the prediction skill. The MME ranked probability skill score is higher than that of individual models, showing the advantage of a multimodel ensemble. Observed Region-A rainfall anomalies are strongly associated with the North Atlantic Oscillation, but none of the models reproduce this relation, which may explain the low skill over Region-A. The superior quality of multimodel ensemble compared with individual models is mainly due to larger ensemble size.

  20. Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation

    Science.gov (United States)

    Ehsan, Muhammad Azhar; Tippett, Michael K.; Almazroui, Mansour; Ismail, Muhammad; Yousef, Ahmed; Kucharski, Fred; Omar, Mohamed; Hussein, Mahmoud; Alkhalaf, Abdulrahman A.

    2017-05-01

    Northern Hemisphere winter precipitation reforecasts from the European Centre for Medium Range Weather Forecast System-4 and six of the models in the North American Multi-Model Ensemble are evaluated, focusing on two regions (Region-A: 20°N-45°N, 10°E-65°E and Region-B: 20°N-55°N, 205°E-255°E) where winter precipitation is a dominant fraction of the annual total and where precipitation from mid-latitude storms is important. Predictability and skill (deterministic and probabilistic) are assessed for 1983-2013 by the multimodel composite (MME) of seven prediction models. The MME climatological mean and variability over the two regions is comparable to observation with some regional differences. The statistically significant decreasing trend observed in Region-B precipitation is captured well by the MME and most of the individual models. El Niño Southern Oscillation is a source of forecast skill, and the correlation coefficient between the Niño3.4 index and precipitation over region A and B is 0.46 and 0.35, statistically significant at the 95 % level. The MME reforecasts weakly reproduce the observed teleconnection. Signal, noise and signal to noise ratio analysis show that the signal variance over two regions is very small as compared to noise variance which tends to reduce the prediction skill. The MME ranked probability skill score is higher than that of individual models, showing the advantage of a multimodel ensemble. Observed Region-A rainfall anomalies are strongly associated with the North Atlantic Oscillation, but none of the models reproduce this relation, which may explain the low skill over Region-A. The superior quality of multimodel ensemble compared with individual models is mainly due to larger ensemble size.

  1. Comment on "Forecasting dengue vaccine demand in disease endemic and non-endemic countries" Amarasinghe et al; Human Vaccines 2010; 6:9, 745-753.

    Science.gov (United States)

    Miller, Nicholas

    2011-01-01

    Recent forecasts of dengue travel vaccine demand, while worthy, might be improved by modelling future travel flows, and by accounting for incremental reductions in demand at the different points in the sequence of events leading to travel vaccine purchase. In particular, we suggest that an alternative method of projecting dengue travel vaccine uptake would account for (1) future flows of travellers from all non-endemic source to all endemic destination countries, based on data that are comparable between countries, and corrected for double-counting and other sources of error; (2) the proportion of such travellers that seek premedical travel advice within a timescale compatible with the probable dengue vaccine schedule; (3) the proportion of these travellers that will present with a combination of risk factors (above and beyond destination country) sufficient to prompt a physician to prescribe a dengue vaccine; and (4) the proportion of these travellers that actually purchase a vaccine when advised to do so.

  2. 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.

  3. 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

    2012-02-01

    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 significant under-predictions for spring months with 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.

  4. The consistency evaluation of the climate version of the Eta regional forecast model developed for regional climate downscaling

    CERN Document Server

    Pisnichenko, I A

    2007-01-01

    The regional climate model prepared from Eta WS (workstation) forecast model has been integrated over South America with the horizontal resolution of 40 km for the period of 1961-1977. The model was forced at its lateral boundaries by the outputs of HadAMP. The data of HadAMP represent the simulation of modern climate with the resolution about150 km. In order to prepare climate regional model from the Eta forecast model was added new blocks and multiple modifications and corrections was made in the original model. The running of climate Eta model was made on the supercomputer SX-6. The detailed analysis of the results of dynamical downscaling experiment includes an investigation of a consistency between the regional and AGCM models as well as of ability of the regional model to resolve important features of climate fields on the finer scale than that resolved by AGCM. In this work we show the results of our investigation of the consistency of the output fields of the Eta model and HadAMP. We have analysed geo...

  5. 基于产运系数法的物流需求量预测研究%Forecasting of Logistic Demand Based on Production and Transportation Coefficient

    Institute of Scientific and Technical Information of China (English)

    冯淑贞

    2013-01-01

    结合我国社会物流统计制度确立的指标体系,对传统的以货运量为核心的物流预测方法的优缺点进行了分析.在此基础上提出了产运系数法,是从物流基本概念出发的物流需求量计算方法,并采用该方法对某地区的运输量、库存量、配送量、全社会物流需求量、第三方物流需求量进行了预测.%In this paper,in connection with the index system determined in the social logistics statistics system of China,we analyzed the strength and weakness of the traditional logistics forecasting processes centered on cargo transport volume,on the basis of which,we proposed the logistics demand calculation method based on production and transportation coefficient,and applied it to the forecasting of the third party logistics demand ofacertain area.

  6. Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

    Science.gov (United States)

    Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

    2016-06-01

    Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

  7. A Regional Ensemble Forecast System for Stratiform Precipitation Events in Northern China.Part Ⅰ: A Case Study

    Institute of Scientific and Technical Information of China (English)

    ZHU Jiangshan; Fanyou KONG; LEI Hengchi

    2012-01-01

    A single-model,short-range,ensemble forecasting system (Institute of Atmospheric Physics,Regional Ensemble Forecast System,IAP REFS) with 15-km grid spacing,configured with multiple initial conditions,multiple lateral boundary conditions,and multiple physics parameterizations with 11 ensemble members,was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China.This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework.The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System (NCEP GEFS) forecasts,and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region.Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system.The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system.The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts.Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts (PQPF).However,the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables,and its influence on PQPF resolution was limited as well.

  8. 24-Hour Forecasting of CME/Flare Eruptions from Active-Region Magnetograms (Invited)

    Science.gov (United States)

    Falconer, D. A.; Barghouty, A.; Khazanov, I. G.; Moore, R. L.

    2010-12-01

    We have developed an automated tool for forecasting severe space weather from full-disk magnetograms. This tool is now being used on a trial basis by NASA’s Space Radiation Analysis Group (SRAG) at JSC. SRAG is responsible for the monitoring and forecasting of exposure the astronauts to particle radiation. The tool is described in Falconer, Barghouty, Khazanov, and Moore (2010), submitted to Space Weather. The new software tool is designed for the empirical forecasting of M- and X-class flares, coronal mass ejections, and solar energetic particle events. For each of these event types, the algorithm is based on the empirical relationship between the event rate and a proxy of the active region’s free magnetic energy. The relationship is determined from ~40,000 active-region magnetograms from ~1,300 active regions that were observed within 30 heliographic degrees from disk center by SOHO/MDI, and that have known histories of flare, coronal mass ejection, and solar energetic particle event production during disk passage. The tool automatically extracts each strong-field magnetic areas from an MDI full-disk magnetogram, identifies each as a NOAA active region, and measures the proxy of the active region’s free magnetic energy from the extracted magnetogram. For each active region, the empirical relationship is then used to convert the free magnetic energy proxy into the active region’s expected event rate (see figure). The expected event rate in turn can be readily converted into the probability that the active region will produce such an event in a given forward time window. We can make this tool applicable to the full-disk line-of-sight magnetograms from SDO/HMI or as a backup, from NSO/GONG. By empirically determining the conversion of the value of free-energy proxy measured from an HMI magnetogram to that which would be measured from an MDI magnetogram, we can use the HMI magnetograms with the empirical relationships determined from our MDI data base to make

  9. Optimal management of water resources demand and supply in irrigated agriculture from plot to regional scale

    Science.gov (United States)

    Schütze, Niels; Wagner, Michael

    2016-04-01

    Growing water scarcity in agriculture is an increasing problem in future in many regions of the world. For assessing irrigation as a measure to increase agricultural water security a generalized stochastic optimization framework 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. Different open loop and closed loop control strategies are evaluated within this stochastic optimization framework in order to generate reliable stochastic crop water production functions (SCWPF). The resulting database of 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.

  10. Determination of Customers' Demand and Expectations in the Auction Sales of Artvin Regional Directorate of Forestry

    Directory of Open Access Journals (Sweden)

    Atakan Öztürk

    2010-11-01

    Full Text Available In this study, sales of auctions were evaluated according to customers' demand and expectations in Artvin Regional Directorate of Forestry. For this purpose, face-to-face survey was conducted with 50 participating in auction sales. As a result, some of the findings and recommendations were made to increase the effectiveness of marketing activities for decision makers.

  11. Developing and testing solar irradiance forecasting techniques in the Hawaiian Islands region

    Science.gov (United States)

    Matthews, D. K.; Souza, J. M.; Stein, K.

    2014-12-01

    Irradiance variability, primarily driven by cloud formation and advection, can be problematic in the state of Hawaíi, because of the high penetration of distributed solar and the small scale of the island electrical grids. The Hawaíi Natural Energy Institute (HNEI) is developing an operational system in order to research and test new techniques to generate solar forecasts for the Hawaiian Islands. The operational system comprises the following three components.(i) A ground-observation-based advection model, using sky imagers and a ceilometer located at the University of Hawaíi at Mānoa. Every 10 minutes (during daylight hours), this component generates a high-resolution 1 hour Global Horizontal Irradiance (GHI) prediction for a region that is within ~15 km of the instrumentation. (ii) A satellite-image-based advection model, using Geostationary Operational Environmental Satellite (GOES) imagery and the Heliosat-II method. Every 30 minutes (during daylight hours), this component generates a 1 km resolution, 6 hour GHI prediction for the entire Hawaiian Archipelago. (iii) A coupled ocean-atmosphere model, using the Regional Ocean Modeling System (ROMS) model and the Weather Research and Forecasting (WRF) model, including newly available microphysics, shallow convection parameterization, and radiative transfer model options. Nightly, this component generates 48 hour GHI, Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) predictions for (a) a 10 km resolution domain covering the full Hawaiian Archipelago and (b) a nested 2 km resolution domain covering the islands of Maui, Óahu, and Hawaíi. We discuss the development and validation of the system, and the scales of forecasting accuracy for each component. We also examine the impact of the coupled model on the simulations of surface flux processeses and ocean-atmosphere feedbacks, both of which influence the prediction of regional cloud properties.

  12. Methodology to determine regional water demand for instream flow and its application in the Yellow River Basin

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yuan; YANG Zhi-feng; Wang Xi-qin

    2006-01-01

    In order to realistically reflect the difference between regional water demand for instream flow and river ecological water demand as well as to resolve the problem that water demand may be counted repeatedly, a concept of regional water demand for minimum instream flow have been developed. The concept was used in the process of determining river functions and calculating ecological water demand for a river. The Yellow River watershed was used to validate the calculation methodology for regional water demand. Calculation results indicate that there are significant differences in water demands among the different regions. The regionalwater demand at the downstream of the Yellow River is the largest about 14.893 × 109 m3/a. The regional water demand of upstream,Lanzhou-Hekou section is the smallest about -5.012×109 m3/a. The total ecological water demand of the Yellow River Basin is 23.06 × 109 m3/a, about the 39% of surface water resources of the Yellow River Basin. That means the maximum available surface water resources should not exceed 61% in the Yellow River Basin. The regional river ecological water demands at the Lower Section of the Yellow River and Longyangxia-Lanzhou Section exceed the surface water resources produced in its region and need to be supplemented from other regions through the water rational planning of watershed water resources. These results provides technical basis for rational plan of water resources of the Yellow River Basin.

  13. 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)

  14. On the Imminent Regional Seismic Activity Forecasting Using INTERMAGNET and Sun-Moon Tide Code Data

    CERN Document Server

    Mavrodiev, Strachimir Cht; Kikuashvili, Giorgi; Botev, Emil; Getsov, Petar; Mardirossian, Garo; Sotirov, Georgi; Teodossiev, Dimitar

    2016-01-01

    In this paper we present an approach for forecasting the imminent regional seismic activity by using geomagnetic data and Earth tide data. The time periods of seismic activity are the time periods around the Sun-Moon extreme of the diurnal average value of the tide vector module. For analyzing the geomagnetic data behaviour we use diurnal standard deviation of geomagnetic vector components F for calculating the time variance Geomag Signal. The Sun storm influence is avoided by using data for daily A-indexes (published by NOAA). The precursor signal for forecasting the incoming regional seismic activity is a simple function of the present and previous day Geomag Signal and A-indexes values. The reliability of the geomagnetic when, regional precursor is demonstrated by using statistical analysis of day difference between the times of predicted and occurred earthquakes. The base of the analysis is a natural hypothesis that the predicted earthquake is the one whose surface energy density in the monitoring point i...

  15. Global competencies of regional stem cell research: bibliometrics for investigating and forecasting research trends.

    Science.gov (United States)

    Watatani, Kenji; Xie, Zhongquan; Nakatsuji, Norio; Sengoku, Shintaro

    2013-09-01

    We employed a bibliometric approach to examine regional stem cell research in the USA, the UK, Japan and China based on publications from 2007 to 2011 with a co-citation clustering analysis to identify region-specific clusters of global competencies. We observed that there are clear differences in the number and interdisciplinary spread of competencies across regions: the USA retains the largest capacity and capability for pursuing medical and pharmaceutical applications; China has shown substantial growth through fusion approaches with chemistry and material sciences; Japan has been pursuing basic biology and is currently seeking further growth; and the UK has shown considerable growth and quality with a focus on medical research and the widest interdisciplinary spread. Furthermore, we discuss policy implications from these results in terms of industrial and clinical applications. These findings provide a rational way of evaluating research policies and forecasting research trends.

  16. Case studies of seasonal rainfall forecasts for Hong Kong and its vicinity using a regional climate model

    Science.gov (United States)

    David Hui; Karen Shum; Ji Chen; Shyh-Chin Chen; Jack Ritchie; John Roads

    2007-01-01

    Seasonal climate forecasts are one of the most promising tools for providing early warnings for natural hazards such as floods and droughts. Using two case studies, this paper documents the skill of a regional climate model in the seasonal forecasting of below normal rainfall in southern China during the rainy seasons of July–August–September 2003 and April–...

  17. 基于路网容量的商圈停车需求预测研究%Forecasting Research of Business Circle Parking-Demand Based on Capacity-of-Network

    Institute of Scientific and Technical Information of China (English)

    钟志新

    2015-01-01

    分析商圈停车、交通拥堵与商圈持续繁荣的关系,商圈停车需求预测对商圈的可持续发展起着重要作用。为合理预见与科学估算商圈停车需求,结合路网容量、区位关系提出基于路网容量的停车需求预测模型。该模型一方面能平衡区域停车供需,缓解静态设施供需矛盾;另一方面能保证停车需求与路网容量相匹配,对动态交通起到必要的控制作用,缓解商圈周边路网的交通拥堵。最后,通过实例分析证明该模型的可行性。%Analyzed the relationship between business circle of parking trafticjam and the prosperity ,traffic congestion paly the vital role for the business circle continuing boom in sustainable .In order to estimate and reasonable foresee ability the business circle of parking demand ,this paper proposed demand forecasting model based on road network capacity and regional relationship .On the one hand ,the model can balance relationship of parking supply and demand ,alleviate the contradiction between supply and demand of static facilities ;on the other hand can guarantee parking demand match with road network capacity ,the necessary control action to dynamic traffic ,alleviate business circle network traffic congestion .At last ,through a case analysis confirmed the feasibility of the model .

  18. Demand forecasting for companies with many branches, low sales numbers per product, and non-recurring orderings

    CERN Document Server

    Kurz, Sascha

    2008-01-01

    We propose the new Top-Dog-Index to quantify the historic deviation of the supply data of many small branches for a commodity group from sales data. On the one hand, the common parametric assumptions on the customer demand distribution in the literature could not at all be supported in our real-world data set. On the other hand, a reasonably-looking non-parametric approach to estimate the demand distribution for the different branches directly from the sales distribution could only provide us with statistically weak and unreliable estimates for the future demand. Based on real-world sales data from our industry partner we provide evidence that our Top-Dog-Index is statistically robust. Using the Top-Dog-Index, we propose a heuristics to improve the branch-dependent proportion between supply and demand. Our approach cannot estimate the branch-dependent demand directly. It can, however, classify the branches into a given number of clusters according to an historic oversupply or undersupply. This classification ...

  19. 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.

  20. Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

    Science.gov (United States)

    Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

    2017-04-01

    The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

  1. Regional moisture balance control of landslide motion: implications for landslide forecasting in a changing climate

    Science.gov (United States)

    Coe, Jeffrey A.

    2012-01-01

    I correlated 12 years of annual movement of 18 points on a large, continuously moving, deep-seated landslide with a regional moisture balance index (moisture balance drought index, MBDI). I used MBDI values calculated from a combination of historical precipitation and air temperature data from A.D. 1895 to 2010, and downscaled climate projections using the Intergovernmental Panel on Climate Change A2 emissions scenario for 2011–2099. At the landslide, temperature is projected to increase ~0.5 °C/10 yr between 2011 and 2099, while precipitation decreases at a rate of ~2 mm/10 yr. Landslide movement correlated with the MBDI with integration periods of 12 and 48 months. The correlation between movement and MBDI suggests that the MBDI functions as a proxy for groundwater pore pressures and landslide mobility. I used the correlation to forecast decreasing landslide movement between 2011 and 2099, with the head of the landslide expected to stop moving in the mid-21st century. The MBDI, or a similar moisture balance index that accounts for evapotranspiration, has considerable potential as a tool for forecasting the magnitude of ongoing deep-seated landslide movement, and for assessing the onset or likelihood of regional, deep-seated landslide activity.

  2. 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

  3. 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).

  4. Store within a store selection based on the demand forecast%基于需求预测的店中店模式决策

    Institute of Scientific and Technical Information of China (English)

    滕文波; 庄贵军

    2012-01-01

    Based on Yue's demand forecast model, this paper studied the impact of product substitutabil-ity, market volatility and demand forecast on the profit of the channel members under SWS (store within a store) and traditional mode. Then we showed how the manufactory chooses its channel. We find that the manufactory prefers the SWS mode when the product substitutability is low. Secondly when the market volatility is high, the manufactory would choose the SWS mode. Lastly when the demand forecast of the manufactory is improved, it should prefer the SWS mode; otherwise it would choose the traditional mode. And when some conditions are satisfied, SWS mode is a Pareto strategy for both of the channel members.%基于Yue等人的需求预测模型,研究了产品替代度、市场波动以及渠道成员需求预测精度对店中店模式和传统模式下渠道成员收益的影响,并以此为基础说明了制造商在不同情况下应如何选择销售模式.研究表明:第一,在产品替代度较小时,制造商和零售商均倾向于选择店中店这一销售模式,反之,则双方更倾向于选择传统模式;第二,当市场波动较大时,制造商更倾向于采用店中店模式,反之,则双方更倾向于选择传统模式,同时在一定的市场波动范围内,采用店中店模式对渠道双方均有利;第三,店中店模式下制造商的需求预测精度越高,制造商越倾向于采用店中店模式,且该需求预测提高到一定的程度后,采用店中店模式对渠道双方均有利.

  5. 商洛市房地产市场需求预测研究%Research on Demand Forecasting Model of Shangluo Real Estate Market

    Institute of Scientific and Technical Information of China (English)

    杨瑛娟

    2015-01-01

    Through the analysis of the five influence factors from 2006 to 2013, including Shangluo real estate average prices, population, disposable income of urban residents, per capita living space, GNP, demand forecasting model of Shangluo real estate market is built using multiple linear regression method. Then the values of Arguments are calculated by least square method. Ultimately the demands for real estate from 2014 to 2016 are predicted, obtain a trend that Shangluo City real estate market demand will steadily grow in the next three years.%通过对2006—2013年商洛市的平均房价、人口、城镇居民可支配收入、人均住房面积、国民生产总值五个影响因素进行分析,运用多元线性回归方法进行分析,构建商洛房地产市场需求预测模型,并运用最小二乘法得出2014—2016年的各自变量的值,预测了商洛市未来3年的房地产市场需求,整体呈现稳定增长的趋势。

  6. 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

  7. 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.

  8. 我国纤维素醚市场调查及需求预测%Market Research and Supply and Demand Forecast of Cellulose Ether in China

    Institute of Scientific and Technical Information of China (English)

    卜新平

    2011-01-01

    The general situation of production and consumption of cellulose ether, especially for caroboxylic methyl cellulose, methyl cellulose and derivatives are introduced. The demand and supply trend is also forecasted. Competition factors and problem in cellulose ether industry are analyzed and gives some suggestions on the domestic production.%重点介绍羟甲基纤维素和甲基纤维素及其衍生物的生产和消费情况,并对市场需求进行了预测。还对纤维素醚的行业竞争因素和存在的问题进行了分析。

  9. Using initial and boundary condition perturbations in medium-range regional ensemble forecasting with two nested domains

    Science.gov (United States)

    Jiang, J.; Koracin, D.; Vellore, R.; Xiao, M.; Lewis, J. M.

    2010-12-01

    Simulated evolution of climate and weather is sensitive to the specification of their initial state. Small errors in the initial state could lead the forecast into a different direction. It is essential to estimate the impact of the uncertainty in initial conditions on the forecast accuracy. For limited-area or regional forecasting, lateral boundary conditions also have considerable influence on the development of mesoscale or local-scale phenomena. Strong lateral boundary conditions derived from a larger scale environment could significantly alter or even remove local-scale components. This study investigates the impact of uncertainty in initial and lateral boundary conditions on medium-range regional forecasting using the Advanced Weather Research and Forecasting (WRF) model. The WRF model was configured with two nested domains: the parent domain has a 108 km horizontal resolution, and a nested domain with 36 km resolution covers the western U.S. The ensemble forecasting was conducted with 50 ensemble members using random perturbations in the initial conditions (ICs) and lateral boundary conditions (LBCs). A case period of 15 days in December 2008 is chosen, during which two intense frontal passages occurred in the western U.S. Results show that, applying only IC perturbations, the contribution from the IC perturbations to the ensemble spread decreases with time. Using both randomly perturbed LBCs and ICs from the coarser domain, the inner nested domain shows a wider ensemble spread. The resulting ensemble forecasting can be interpreted as a probabilistic prediction for wind energy, especially for wind gust and wind turbine operational cut-off. The analysis also includes an efficiency comparison of using coarser ensemble forecasting vs. a higher resolution single control run.

  10. Integrated management of water resources demand and supply in irrigated agriculture from plot to regional scale

    Science.gov (United States)

    Schütze, Niels; Wagner, Michael

    2016-05-01

    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.

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. Pan-European household and industrial water demand: regional relevant estimations

    Science.gov (United States)

    Bernhard, Jeroen; Reynaud, Arnaud; de Roo, Ad

    2016-04-01

    Sustainable water management is of high importance to provide adequate quality and quantity of water to European households, industries and agriculture. Especially since demographic, economic and climate changes are expected to increase competition for water between these sectors in the future. A shortage of water implies a reduction in welfare of households or damage to economic sectors. This socio-economic component should be incorporated into the decision-making process when developing water allocation schemes, requiring detailed water use information and cost/benefit functions. We now present the results of our study which is focused at providing regionally relevant pan-European water demand and cost-benefit estimations for the household and industry sector. We gathered consistent data on water consumption, water prices and other relevant variables at the highest spatial detail available from national statistical offices and other organizational bodies. This database provides the most detailed up to date picture of present water use and water prices across Europe. The use of homogeneous data allowed us to compare regions and analyze spatial patterns. We applied econometric methods to determine the main determinants of water demand and make a monetary valuation of water for both the domestic and industry sector. This monetary valuation is important to allow water allocation based on economic damage estimates. We also attempted to estimate how population growth, as well as socio-economic and climatic changes impact future water demand up to 2050 using a homogeneous method for all countries. European projections for the identified major drivers of water demand were used to simulate future conditions. Subsequently, water demand functions were applied to estimate future water use and potential economic damage caused by water shortages. We present our results while also providing some estimation of the uncertainty of our predictions.

  16. Demand for Energy and Energy Generation: Does Regional Energy Policy Play a Role?

    Directory of Open Access Journals (Sweden)

    Paul OJEAGA

    2014-06-01

    Full Text Available Does regional energy policy play a role in regional energy generation? What does the implication of the current industrialization trend mean for the generation and the supply process across regions? And to what extent does regional energy policy affect energy security (energy supply risks in regions? This study investigates the effect of regional energy policy on regional generation characteristics in seven regions of the World using regional panel data from 1980 to 2010 a period of 31 years although some years of data are missing. It was found that regional energy policy were been shaped by pollution concerns and that cost reduction needs had strong effects on energy security (energy generation resources supply. The method of estimation used is the quantile regression estimation method which provides robust estimates after controlling for heterscedastic errors and is robust in the presence of outliers in the response measurement. Energy policy has strong implication for access to sustainable supply of energy generation resources however it had little or no effect on energy generation itself. Industrial demand for energy particularly in the developed countries were probably also making developed countries depend on more nuclear and hydro energy generation sources.

  17. Short-term load forecasting based on a multi-model

    Energy Technology Data Exchange (ETDEWEB)

    Faller, C. [ETH, Zurich (Switzerland). Faculty of Electrical Engineering; Dvorakova, R.; Horacek, P. [Czech Technical University (Czech Republic). Faculty of Electrical Engineering

    2000-07-01

    Two algorithms for short-term electricity demand forecasting in the regional electricity distribution network are presented. Several approaches - feedforward neural network, adaptive modelling and fuzzy modelling - are applied to the forecast. Two different models are designed. A one hour forecasting is based on the General Regression Neural Network (GRNN) model and Principle Component Analysis. The multi-model with adaptive features and fuzzy reasoning is used for a longer-term forecast. (author)

  18. STEEL CONSUMPTION DEMAND FORECASTING FOR AUTOMOBILE MANUFACTURING COMPANIES%汽车制造企业钢材用量需求预测技术

    Institute of Scientific and Technical Information of China (English)

    杨晓东; 缪欣明; 徐广卿

    2012-01-01

    Aiming at reasonable forecasting the steel consumption demand in automobile manufacturing companies, we put forward a set of forecasting technologies suitable for automobile manufacturing companies with full consideration of the impact of past historical data in corresponding months and the data of recent months. The model realises the scientific utilisation of historical data and also reveals the development trends in specific time period at present, so it takes into account the influences of both seasons and policy volatility, and meliorates the rea-sonability and creditability of the forecasting result. Relevant model has been applied to production practice, and a corresponding " visualised tracking system for demand-driven automotive steel" has been developed as well. It effectively improves the fulfilment rates of customers' order , and greatly facilitates the arrangement of production schedule in corporations too. This frames a considerable economic benefit to the enterprises , and has promotional value in further application.%针对汽车制造企业钢材用量需求的合理预测问题,充分考虑到以往相应月份的历史数据和近期各月份数据的影响,建立一套适合于汽车制造企业的钢材实际需求预测技术,不但实现了对历史数据的科学利用,而且体现了当前特定时间段的发展趋势,最大程度地兼顾了季节和政策波动的影响,提高了预测结果的合理性和可信性.相关模型已经被推广应用到生产实践,开发出了相应的“可视化汽车用钢需求拉动跟踪系统”,不但有效地提高了客户订货满足率,且大大方便了企业对生产计划的编排,给企业创造了较大的经济效益,具有进一步推广应用的价值.

  19. 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.

  20. Impact on the short-term forecast using radar data assimilation on the South and Southeast region of Brazil

    Science.gov (United States)

    Herdies, Dirceu; Viana, Liviany; Souza, Diego; Vendrasco, Eder

    2017-04-01

    The objective of this study was to analyze the behavior of the precipitation related to the numerical weather forecast employing the Atmospheric Weather Research and Forecasting model (WRF) and the Data assimilation Weather Research and Forecasting model Data Assimilation Three Dimensional-Variational (WRFDA / 3D-VAR) system for a Convective system occurred in the summer of 2015/2016 on the southern and southeastern regions of Brazil. The datasets used were radar data in the region of interest and observational data from the Global Telecommunications System (GTS). The data assimilated were radial velocity (directly) and reflectivity (indirectly) and variables of the state - air temperature, surface pressure, wind speed and direction, among others. Three experiments were performed to evaluate the weather forecast for the selected case: i) without any type of assimilation, (ii) assimilated GTS data, and (iii) assimilated data from available radars. The prediction until to 6 hours of convective system intensity was evaluated, which were validated with the combined precipitation data from satellites and surface. The results showed the positive impact of the short-term forecast using experiments with the radar and GTS data when compared to the experiment without using them. Thus, this study is expected to contribute to the development of modeling and the operation of the assimilation of radar data in the numerical weather prediction over the regions of study.

  1. 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.

  2. 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.

  3. 小波分析和支持向量机组合法预测应急血液需求研究%Research on WA-SVM Combined Method for Forecasting Blood Demand in Emergency Rescue

    Institute of Scientific and Technical Information of China (English)

    朱莎; 刘晓

    2013-01-01

    针对地震紧急救援阶段血液需求特点,提出用于预测其需求量的,基于小波分析(WA)和支持向量机(SVM)的组合方法(WA-SVM).首先对原始血液需求进行小波分析,然后确定SVM输入向量和输出向量集合,构建各层序列的SVM预测模型,对血液需求进行预测.汶川地震案例表明,该方法的预测精度优于经验模态分解和SVM的组合预测模型以及SVM单项预测模型.%A combined method of WA and SVM was worked out for forecasting the blood demand in emergency rescue after earthquake.The sample demand series was first mapped into several different frequency scales using wavelet transformation,and then a SVM is established for each scale.The WA-SVM forecasting method was then used to forecast blood demand in emergency rescue taking a case in Wenchuan earthquake as an example.The forecasting results indicate that the WA-SVM forecasting method has better performance both in accuracy and applicability in comparison with the empirical mode decomposition (EMD)-SVM combined method,and SVM method.

  4. 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.

  5. Long Term Energy Consumption Forecasting Using Genetic Programming

    OpenAIRE

    KARABULUT, Korhan; Alkan, Ahmet; YILMAZ, Ahmet

    2008-01-01

    Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information, such as climate and previous load demand data. In this paper, a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. ...

  6. Comparing One-way and Two-way Coupled Hydrometeorological Forecasting Systems for Flood Forecasting in the Mediterranean Region

    Science.gov (United States)

    Givati, Amir; Gochis, David; Rummler, Thomas; Kunstmann, Harald; Yu, Wei

    2016-04-01

    A pair of hydro-meteorological modeling systems were calibrated and evaluated for the Ayalon basin in central Israel to assess the advantages and limitations of one-way versus two-way coupled modeling systems for flood prediction. The models used included the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) model and the Weather Research and Forecasting (WRF) Hydro modeling system. The models were forced by observed, interpolated precipitation from rain-gauges within the basin, and with modeled precipitation from the WRF atmospheric model. Detailed calibration and evaluation was carried out for two major winter storms in January and December 2013. Then both modeling systems were executed and evaluated in an operational mode for the full 2014/2015 rainy season. Outputs from these simulations were compared to observed measurements from hydrometric stations at the Ayalon basin outlet. Various statistical metrics were employed to quantify and analyze the results: correlation, Root Mean Square Error (RMSE) and the Nash-Sutcliffe (NS) efficiency coefficient. Foremost, the results presented in this study highlight the sensitivity of hydrological responses to different sources of precipitation data, and less so, to hydrologic model formulation. With observed precipitation data both calibrated models closely simulated the observed hydrographs. The two-way coupled WRF/WRF-Hydro modeling system produced improved both the precipitation and hydrological simulations as compared to the one-way WRF simulations. Findings from this study suggest that the use of two-way atmospheric-hydrological coupling has the potential to improve precipitation and, therefore, hydrological forecasts for early flood warning applications. However more research needed in order to better understand the land-atmosphere coupling mechanisms driving hydrometeorological processes on a wider variety precipitation and terrestrial hydrologic systems.

  7. Comparing One-Way and Two-Way Coupled Hydrometeorological Forecasting Systems for Flood Forecasting in the Mediterranean Region

    Directory of Open Access Journals (Sweden)

    Amir Givati

    2016-05-01

    Full Text Available A pair of hydro-meteorological modeling systems were calibrated and evaluated for the Ayalon basin in central Israel to assess the advantages and limitations of one-way versus two-way coupled modeling systems for flood prediction. The models used included the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS model and the Weather Research and Forecasting (WRF Hydro modeling system. The models were forced by observed, interpolated precipitation from rain-gauges within the basin, and with modeled precipitation from the WRF atmospheric model. Detailed calibration and evaluation was carried out for two major winter storms in January and December 2013. Then, both modeling systems were executed and evaluated in an operational mode for the full 2014/2015 rainy season. Outputs from these simulations were compared to observed measurements from the hydrometric station at the Ayalon basin outlet. Various statistical metrics were employed to quantify and analyze the results: correlation, Root Mean Square Error (RMSE and the Nash–Sutcliffe (NS efficiency coefficient. Foremost, the results presented in this study highlight the sensitivity of hydrological responses to different sources of simulated and observed precipitation data, and demonstrate improvement, although not significant, at the Hydrological response, like simulated hydrographs. With observed precipitation data both calibrated models closely simulated the observed hydrographs. The two-way coupled WRF/WRF-Hydro modeling system produced improved both the precipitation and hydrological simulations as compared to the one-way WRF simulations. Findings from this study, as well as previous studies, suggest that the use of two-way atmospheric-hydrological coupling has the potential to improve precipitation and, therefore, hydrological forecasts for early flood warning applications. However, more research needed in order to better understand the land-atmosphere coupling mechanisms

  8. 国内外国家免疫规划疫苗需求量预测方法综述%Review on the Forecast Methods of Vaccines Demand for National Immunization Programme in China and Abroad

    Institute of Scientific and Technical Information of China (English)

    姜晓飞

    2013-01-01

    Vaccines demand forecast is the basic for ensure the high quality and adequate immunization supplies.It helps policymakers to prepare the budget and allocate vaccines.It is the precondition to know the forecasting methods and influence factors for making plan of vaccine supply.In this paper,we will review the methods of forecast demands of vaccine and influence factors in China and abroad.%预防接种疫苗需求量预测,是保证高质量充足疫苗供应的基础,有利于指导疫苗概算和分配计划.预测方法和影响因素的确定,是制定疫苗需求计划的前提条件.现对国内外疫苗需求量预测的方法与有关的影响因素进行综述.

  9. 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.

  10. 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...

  11. Demand-driven water withdrawals by Chinese industry: a multi-regional input-output analysis

    Institute of Scientific and Technical Information of China (English)

    Bo ZHANG; Z.M.CHEN; L.ZENG; H.QIAO; B.CHEN

    2016-01-01

    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 demanddriven 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 watersaving 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.

  12. A Regional Ensemble Forecast System for Stratiform Precipitation Events in the Northern China Region.Part Ⅱ: Seasonal Evaluation for Summer 2010

    Institute of Scientific and Technical Information of China (English)

    ZHU Jiangshan; KONG Fanyou; LEI Hengchi

    2013-01-01

    In this study,the Institute of Atmospheric Physics,Chinese Academy of Sciences-regional ensemble forecast system (IAP-REFS) described in Part Ⅰ was further validated through a 65-day experiment using the summer season of 2010.The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts (QPF) and probabilistic QPF,but it has a systematic bias in forecasting near-surface variables.Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts.In this study,the perturbation extraction and inflation method (proposed with the single case study in Part Ⅰ) was further applied to the full season with different inflation factors.This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables.The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.

  13. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    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...

  14. 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...

  15. A hierarchical Bayesian model for regionalized seasonal forecasts: Application to low flows in the northeastern United States

    Science.gov (United States)

    Ahn, Kuk-Hyun; Palmer, Richard; Steinschneider, Scott

    2017-01-01

    This study presents a regional, probabilistic framework for seasonal forecasts of extreme low summer flows in the northeastern United States conditioned on antecedent climate and hydrologic conditions. The model is developed to explore three innovations in hierarchical modeling for seasonal forecasting at ungaged sites: (1) predictive climate teleconnections are inferred directly from ocean fields instead of predefined climate indices, (2) a parsimonious modeling structure is introduced to allow climate teleconnections to vary spatially across streamflow gages, and (3) climate teleconnections and antecedent hydrologic conditions are considered jointly for regional forecast development. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with five simpler nested formulations to test specific hypotheses embedded in the full model structure. Results indicate that each of the three innovations improve out-of-sample summer low-flow forecasts, with the greatest benefits derived from the spatially heterogeneous effect of climate teleconnections. We conclude with a discussion of possible model improvements from a better representation of antecedent hydrologic conditions at ungaged sites.

  16. Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools

    Science.gov (United States)

    Ramírez-Cuesta, J. M.; Cruz-Blanco, M.; Santos, C.; Lorite, I. J.

    2017-03-01

    Reference evapotranspiration (ETo) is a key component in efficient water management, especially in arid and semi-arid environments. However, accurate ETo assessment at the regional scale is complicated by the limited number of weather stations and the strict requirements in terms of their location and surrounding physical conditions for the collection of valid weather data. In an attempt to overcome this limitation, new approaches based on the use of remote sensing techniques and weather forecast tools have been proposed. Use of the Land Surface Analysis Satellite Application Facility (LSA SAF) tool and Geographic Information Systems (GIS) have allowed the design and development of innovative approaches for ETo assessment, which are especially useful for areas lacking available weather data from weather stations. Thus, by identifying the best-performing interpolation approaches (such as the Thin Plate Splines, TPS) and by developing new approaches (such as the use of data from the most similar weather station, TS, or spatially distributed correction factors, CITS), errors as low as 1.1% were achieved for ETo assessment. Spatial and temporal analyses reveal that the generated errors were smaller during spring and summer as well as in homogenous topographic areas. The proposed approaches not only enabled accurate calculations of seasonal and daily ETo values, but also contributed to the development of a useful methodology for evaluating the optimum number of weather stations to be integrated into a weather station network and the appropriateness of their locations. In addition to ETo, other variables included in weather forecast datasets (such as temperature or rainfall) could be evaluated using the same innovative methodology proposed in this study.

  17. 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.

  18. 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...... 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...

  19. Impacts of climate change on sub-regional electricity demand and distribution in the southern United States

    Science.gov (United States)

    Allen, Melissa R.; Fernandez, Steven J.; Fu, Joshua S.; Olama, Mohammed M.

    2016-08-01

    High average temperatures lead to high regional electricity demand for cooling buildings, and large populations generally require more aggregate electricity than smaller ones do. Thus, future global climate and population changes will present regional infrastructure challenges regarding changing electricity demand. However, without spatially explicit representation of this demand or the ways in which it might change at the neighbourhood scale, it is difficult to determine which electricity service areas are most vulnerable and will be most affected by these changes. Here we show that detailed projections of changing local electricity demand patterns are viable and important for adaptation planning at the urban level in a changing climate. Employing high-resolution and spatially explicit tools, we find that electricity demand increases caused by temperature rise have the greatest impact over the next 40 years in areas serving small populations, and that large population influx stresses any affected service area, especially during peak demand.

  20. Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities

    Science.gov (United States)

    Kousari, Mohammad Reza; Hosseini, Mitra Esmaeilzadeh; Ahani, Hossein; Hakimelahi, Hemila

    2015-09-01

    An effective forecast of the drought definitely gives lots of advantages in regard to the management of water resources being used in agriculture, industry, and households consumption. To introduce such a model applying simple data inputs, in this study a regional drought forecast method on the basis of artificial intelligence capabilities (artificial neural networks) and Standardized Precipitation Index (SPI in 3, 6, 9, 12, 18, and 24 monthly series) has been presented in Fars Province of Iran. The precipitation data of 41 rain gauge stations were applied for computing SPI values. Besides, weather signals including Multivariate ENSO Index (MEI), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), NINO1+2, anomaly NINO1+2, NINO3, anomaly NINO3, NINO4, anomaly NINO4, NINO3.4, and anomaly NINO3.4 were also used as the predictor variables for SPI time series forecast the next 12 months. Frequent testing and validating steps were considered to obtain the best artificial neural networks (ANNs) models. The forecasted values were mapped in verification sector then they were compared with the observed maps at the same dates. Results showed considerable spatial and temporal relationships even among the maps of different SPI time series. Also, the first 6 months forecasted maps showed an average of 73 % agreements with the observed ones. The most important finding and the strong point of this study was the fact that although drought forecast in each station and time series was completely independent, the relationships between spatial and temporal predictions remained. This strong point mainly referred to frequent testing and validating steps in order to explore the best drought forecast models from plenty of produced ANNs models. Finally, wherever the precipitation data are available, the practical application of the presented method is possible.

  1. Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities

    Science.gov (United States)

    Kousari, Mohammad Reza; Hosseini, Mitra Esmaeilzadeh; Ahani, Hossein; Hakimelahi, Hemila

    2017-01-01

    An effective forecast of the drought definitely gives lots of advantages in regard to the management of water resources being used in agriculture, industry, and households consumption. To introduce such a model applying simple data inputs, in this study a regional drought forecast method on the basis of artificial intelligence capabilities (artificial neural networks) and Standardized Precipitation Index (SPI in 3, 6, 9, 12, 18, and 24 monthly series) has been presented in Fars Province of Iran. The precipitation data of 41 rain gauge stations were applied for computing SPI values. Besides, weather signals including Multivariate ENSO Index (MEI), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), NINO1+2, anomaly NINO1+2, NINO3, anomaly NINO3, NINO4, anomaly NINO4, NINO3.4, and anomaly NINO3.4 were also used as the predictor variables for SPI time series forecast the next 12 months. Frequent testing and validating steps were considered to obtain the best artificial neural networks (ANNs) models. The forecasted values were mapped in verification sector then they were compared with the observed maps at the same dates. Results showed considerable spatial and temporal relationships even among the maps of different SPI time series. Also, the first 6 months forecasted maps showed an average of 73 % agreements with the observed ones. The most important finding and the strong point of this study was the fact that although drought forecast in each station and time series was completely independent, the relationships between spatial and temporal predictions remained. This strong point mainly referred to frequent testing and validating steps in order to explore the best drought forecast models from plenty of produced ANNs models. Finally, wherever the precipitation data are available, the practical application of the presented method is possible.

  2. Short-term forecast of the carbon monoxide concentration over the Moscow megacity region by COSMO-ART

    Science.gov (United States)

    Kislov, Alexander; Revokatova, Anastasia; Surkova, Galina; Rivin, Gdaliy

    2016-04-01

    Introduction. Atmospheric pollution in the cities is constantly increasing. It makes air pollution forecast extremely important for modern meteorology. Short-range spatial detailed forecast of air pollutants distribution should be done as a part of weather forecast. A short-term forecast of city 'chemical weather' requires real daily data on pollutant emissions. For the operational daily forecast of pollutant concentrations, long-term emissions averages are usually used, which may differ significantly from real emissions on the particular day, especially in big cities. Methodology. The method of calculation of pollutant emissions is described for the short-term forecast. An on-line coupled chemical transport model, COSMO-ART (https://www.imk-tro.kit.edu/english/3509.php), was applied for the Moscow megasity region, Russia. Because it is impossible to have real daily emissions values, the method of emission estimation on the basis of measurements of the concentrations of air pollutants is proposed. The method is based on the assumption that the pollutant concentrations reflect the intensity of emissions sources. The proposed method allows the hourly measurements of air pollutant concentrations to be transformed into emissions values as fast as the measurements can be done. Results. This method is described and its application is shown for carbon monoxide (one of the most dangerous pollutant). Around 90% of CO in Moscow is emitted by traffic. Conclusions. Verification of the COSMO-ART results demonstrates that the calculated emissions gave better results compared with the results from mean emission values (TNO emissions dataset) that were used previously. This approach provides the possibility of running an operational short-term pollutant concentration forecast with more detailed spatial structure.

  3. A Simple Discussion on the Supply and Demand of Water Resources in the Western Region of China

    Institute of Scientific and Technical Information of China (English)

    Yu Hongbo

    2006-01-01

    We are accustomed to solve the problem of the water scarcity in the western region by the thought of increasing the effective supply of water to meet the needs of Go-west Campaign. After introducing the dynamic equilibrium principle on supply and demand in economy,we find that we should solve the problem of the water scarcity in the western region through reducing total demand to achieve the dynamic equilibrium of supply and demand. Finally water resources in the western region can be enlarged by an accumulated way.

  4. Forecasting climate change impacts to plant community composition in the Sonoran Desert region

    Science.gov (United States)

    Munson, Seth M.; Webb, Robert H.; Belnap, Jayne; Hubbard, J. Andrew; Swann, Don E.; Rutman, Sue

    2012-01-01

    Hotter and drier conditions projected for the southwestern United States can have a large impact on the abundance and composition of long-lived desert plant species. We used long-term vegetation monitoring results from 39 large plots across four protected sites in the Sonoran Desert region to determine how plant species have responded to past climate variability. This cross-site analysis identified the plant species and functional types susceptible to climate change, the magnitude of their responses, and potential climate thresholds. In the relatively mesic mesquite savanna communities, perennial grasses declined with a decrease in annual precipitation, cacti increased, and there was a reversal of the Prosopis velutina expansion experienced in the 20th century in response to increasing mean annual temperature (MAT). In the more xeric Arizona Upland communities, the dominant leguminous tree, Cercidium microphyllum, declined on hillslopes, and the shrub Fouquieria splendens decreased, especially on south- and west-facing slopes in response to increasing MAT. In the most xeric shrublands, the codominant species Larrea tridentata and its hemiparasite Krameria grayi decreased with a decrease in cool season precipitation and increased aridity, respectively. This regional-scale assessment of plant species response to recent climate variability is critical for forecasting future shifts in plant community composition, structure, and productivity.

  5. 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.

  6. 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)

  7. Real-Time Local Range On-Demand and Dynamic Regional Range Images

    Energy Technology Data Exchange (ETDEWEB)

    Tsap, L.V.

    2000-02-22

    This paper presents a new approach to a gesture tracking system using real-time range on-demand. The system represents a gesture-controlled interface for interactive visual exploration of large data sets. The paper describes a method performing range processing only when necessary and where necessary. Range data is processed only for non-static regions of interest. This is accomplished by a set of filters on the color, motion, and range data. The speedup achieved is between 41% and 54%. The algorithm also includes a robust skin color segmentation insensitive to illumination changes. Selective range processing results in dynamic regional range images (DRRIs). This development is also placed in a broader context of a biological visual system emulation, specifically redundancies and attention mechanisms.

  8. Real-Time Local Range On-Demand for Tracking Gestures and Dynamic Regional Range Images

    Energy Technology Data Exchange (ETDEWEB)

    Tsap, L.V.

    2000-05-30

    This paper presents a new approach to a gesture-tracking system using real-time range on-demand. The system represents a gesture-controlled interface for interactive visual exploration of large data sets. The paper describes a method performing range processing only when necessary and where necessary. Range data is processed only for non-static regions of interest. This is accomplished by a set of filters on the color, motion, and range data. The speedup achieved is between 41% and 54%. The algorithm also includes a robust skin-color segmentation insensitive to illumination changes. Selective range processing results in dynamic regional range images (DRRIs). This development is also placed in a broader context of a biological visual system emulation, specifically redundancies and attention mechanisms.

  9. Linear and Non-Linear Approaches for Statistical Seasonal Rainfall Forecast in the Sirba Watershed Region (SAHEL

    Directory of Open Access Journals (Sweden)

    Abdouramane Gado Djibo

    2015-09-01

    Full Text Available Since the 90s, several studies were conducted to evaluate the predictability of the Sahelian rainy season and propose seasonal rainfall forecasts to help stakeholders to take the adequate decisions to adapt with the predicted situation. Unfortunately, two decades later, the forecasting skills remains low and forecasts have a limited value for decision making while the population is still suffering from rainfall interannual variability: this shows the limit of commonly used predictors and forecast approaches for this region. Thus, this paper developed and tested new predictors and new approaches to predict the upcoming seasonal rainfall amount over the Sirba watershed. Predictors selected through a linear correlation analysis were further processed using combined linear methods to identify those having high predictive power. Seasonal rainfall was forecasted using a set of linear and non-linear models. An average lag time up to eight months was obtained for all models. It is found that the combined linear methods performed better than non-linear, possibly because non-linear models require larger and better datasets for calibration. The R2, Nash and Hit rate score are respectively 0.53, 0.52, and 68% for the combined linear approach; and 0.46, 0.45, 61% for non-linear principal component analysis.

  10. Hawaii Energy Strategy: Program guide. [Contains special sections on analytical energy forecasting, renewable energy resource assessment, demand-side energy management, energy vulnerability assessment, and energy strategy integration

    Energy Technology Data Exchange (ETDEWEB)

    1992-09-01

    The Hawaii Energy Strategy program, or HES, is a set of seven projects which will produce an integrated energy strategy for the State of Hawaii. It will include a comprehensive energy vulnerability assessment with recommended courses of action to decrease Hawaii's energy vulnerability and to better prepare for an effective response to any energy emergency or supply disruption. The seven projects are designed to increase understanding of Hawaii's energy situation and to produce recommendations to achieve the State energy objectives of: Dependable, efficient, and economical state-wide energy systems capable of supporting the needs of the people, and increased energy self-sufficiency. The seven projects under the Hawaii Energy Strategy program include: Project 1: Develop Analytical Energy Forecasting Model for the State of Hawaii. Project 2: Fossil Energy Review and Analysis. Project 3: Renewable Energy Resource Assessment and Development Program. Project 4: Demand-Side Management Program. Project 5: Transportation Energy Strategy. Project 6: Energy Vulnerability Assessment Report and Contingency Planning. Project 7: Energy Strategy Integration and Evaluation System.

  11. Designing a green gas supply to meet a regional seasonal demand: A case study: 23 juni 2014

    OpenAIRE

    Bekkering, J.; Hengeveld, E.J.; Gemert, W.J.T. van; Broekhuis, A.A.

    2014-01-01

    One of the issues concerning the replacement of natural gas by green gas is the seasonal pattern of the gas demand. When constant production is assumed, this may limit the injected quantity of green gas into a gas grid to the level of the minimum gas demand in summer. A procedure was proposed to increase the gas demand coverage in a geographical region, i.e. the extent to which natural gas demand can be replaced by green gas. This was done by modeling flexibility into farm-scale green gas sup...

  12. Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

    Science.gov (United States)

    Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

    2017-04-01

    Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models

  13. [Pediatric intermediate care demand, results of a survey in 3 French regions].

    Science.gov (United States)

    Thiriez, G; Lefebvre, A

    2010-08-01

    In France, recent legislation for pediatric critical care organization endorsed the creation of a new level of pediatric care, the intermediate care units. These units treat children who require close monitoring and/or continuous monitoring due to potential failure of 1 or more organs, yet not to the degree of intensity of pediatric critical care. These rules do not provide precise admission and discharge guidelines for the pediatric patients requiring intermediate care. We conducted a questionnaire survey in all pediatric units in 3 French regions: Franche-Comté, Nord-Pas-de-Calais, and Rhône-Alpes. We obtained a response rate of 62.2% from the pediatric units. We estimated the pediatric intermediate care demand, with a unit occupancy rate of 80%, at 1.2 beds per 10 pediatric medical beds, 2.6 beds per 10 surgical beds, and 4 beds per 10 critical care unit beds or hemato-oncology beds. The intermediate care demand was higher in university-affiliated hospitals. One-third of the children referred to these units were less than 1 year old, 1/4 were less than 6 months old. We also described the diseases and potential organ failures of the patients who were referred to these intermediate care units. This study provides an estimate of the demand for pediatric intermediate care as defined in the French legislation and therefore helps implement projects to create such units. More specific criteria are still needed to oversee the implementation of these projects. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

  14. 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.

  15. 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

  16. The ScaLIng Macroweather Model (SLIMM) and monthly and inter annual regional forecasting.

    Science.gov (United States)

    Lovejoy, S.; Del Rio Amador, L.; Sloman, L.

    2015-12-01

    By exploiting the sensitive dependence on initial conditions, GCM's can generate a statistical ensemble of future states in which the high frequency "weather" is treated as a driving noise. Following Hasselman, 1976, this has lead to stochastic models that directly generate the noise, and model the low frequencies using systems of integer ordered linear ordinary differential equations, the most well known are the linear inverse models (LIM). These have been presented as a benchmark for decadal surface temperature forecast. Using the LIM, hindcast skills comparable to and sometimes even better than the skill of (coupled) Global Circulation Models (GCM's) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Nevertheless, the short range exponential temporal decorrelations implicit in the LIM models are unrealistic (the true decorrelations are closer to long range power laws), and - as a consequence - the useful limit to the forecast horizon is roughly one year: it enormously underestimates the memory of the system. In presentation, we make a scaling analogue of the LIM: ScaLIng Macroweather Model (SLIMM) that exploits the power law (scaling) behavior in time of the temperature field and consequently, make use of the long history dependence of the data to improve the skill. The results predicted analytically by the model have been tested by performing actual hindcasts in different 5º x 5º regions on the planet using the Twentieth Century Reanalysis as a reference datasets. As a first step, we removed the anthropogenic component of each time series based on its sensitivity to equivalent CO2 concentration for the last 130 years, the residues are our estimates of the natural variability that SLIMM predicts. This residues were treated as fractional Gaussian noise processes with scaling exponent H between -0.5 and 0. The value of H for each grid-point can be obtained directly from the data. We report maps of theoretical skill predicted by the model and we

  17. Agricultural water supply/demand changes under projected future climate change in the arid region of northwestern China

    Science.gov (United States)

    Guo, Ying; Shen, Yanjun

    2016-09-01

    The water resources in the arid region of northwestern China, which are impacted by climate change, tend to be more unstable, and the environment and ecosystems will suffer from severe water shortage. In this paper, potential future climate trends were predicted based on CMIP5 simulations in this region. The water availability and agricultural water demand under future climate change scenarios were estimated. Impacted by increases in temperature, the irrigation water demand will increase by 4.27-6.15 billion m3 in this region over the next 60 years, compared to the demand of 32.75 billion m3 during 1971-2000. However, the annual runoff will only increase by 4.8-8.5 billion m3, which is equivalent to or even less than the increased irrigation water demand. In fact, the increased demand for industrial, domestic and ecological water were not considered here. Thus, the water supply/demand contradiction will result in more severe water shortages in the future. According to a comparison with simulated irrigation water demand under three adaptation strategy scenarios, we should take effective measures such as improving the efficiency of irrigation water utilization, reducing crop planting areas and adjusting crop planting structures to alleviate the impacts of future climate changes and human activities on the water supply and water use in this region.

  18. Movement and physiological demands of international and regional men's touch Rugby matches.

    Science.gov (United States)

    Beaven, Robert P; Highton, Jamie M; Thorpe, Mary-Catherine; Knott, Emma V; Twist, Craig

    2014-11-01

    This study compared the internal and external match demands imposed on international and regional standard male touch rugby players. The study adopted a cohort design with independent groups. Twelve international players (mean age, 27.8 ± 6.2 years; body mass, 72.8 ± 3.7 kg; stature, 174.5 ± 5.4 cm) and 9 regional players (mean age, 25.5 ± 5.5 years; body mass 74.2 ± 7 kg; stature 174.1 ± 7 cm) were analyzed during 9 competitive matches from the 2013 season. Movement demands were measured using a 5-Hz global positioning system, alongside heart rate (HR) and session rating of perceived exertion (s-RPE) to quantify internal load. Total distance covered by international players was lower than regional players (2265.8 ± 562.3 cf. 2970 ± 558.9 m; p ≤ 0.05). However, international players had greater relative distance (137.1 ± 13.6 cf. 126.2 ± 17.2 m·min) due to shorter playing times per match (p ≤ 0.05). Absolute high-speed running (>14 km·h) was not different between groups (p > 0.05), but relative high-speed running (39.3 ± 12.0 cf. 26.0 ± 13.6 m·min) was higher for international players. Regional players performed more absolute low-speed activity (≤14 km·h) than international players (p ≤ 0.05), whereas relative low-speed activity was not different between groups (p > 0.05). Very high-speed running (>20 km·h) distance, bout number and frequency, peak, and average speed were all greater in international players (p ≤ 0.05). Higher average HR, summated HR, and s-RPE (p ≤ 0.05) indicated higher internal loads during matches for regional players. These data indicate that performance in men's touch rugby is characterized by more relative high-speed running and better repeated sprint capacities in higher standard players.

  19. Risk Assessment of Regional Irrigation Water Demand and Supply in an Arid Inland River Basin of Northwestern China

    Directory of Open Access Journals (Sweden)

    Bin Guo

    2015-09-01

    Full Text Available Irrigation water demand accounts for more than 95% of the total water use in the Kaidu-kongqi River Basin. Determination of the spatial and temporal trends in irrigation water demand is important for making sustainable and wise water management strategies in this highly water deficit region. In this study, the spatial and temporal trends in irrigation water demand as well as net crop irrigation water requirements for nine major crops during 1985–2009 were analyzed by combining the Penman-Monteith equation recommended by Food and Agriculture Organization (FAO and GIS technology. The regional water stress was also evaluated based on the total irrigation water demand and river discharge at the annual and monthly scales. The results indicated that the annual irrigation water demand in this arid region showed a significant increasing trend during the past 25 years. Total irrigation water demand increased from 14.68 × 108 m3 in 1985 to 34.15 × 108 m3 in 2009. The spatial pattern of total irrigation water demand was significantly affected by the changes in cotton growing area. Due to differences in crop planting structure, the monthly average irrigation water demands in Korla City and Yuli County amounted to the peak in July, while those in other regions reached the maximum in June. Although the annual river runoff was much larger than the irrigation water demand, there was serious water deficit during the critical water use period in May and June in some dry years. The presented study provides important information for managers and planners on sustainable use of water resources in this arid region.

  20. Complexity Research on Mid-Long Term Forecast of Regional Power Load%区域电力负荷中长期预测复杂性研究

    Institute of Scientific and Technical Information of China (English)

    崔和瑞; 刘冬

    2011-01-01

    为了解决我国各个地区电力需求区域性、结构性波动的问题,保持国民经济和区域经济的持续健康发展,创新性地将区域电力负荷中长期预测作为一个系统研究,并从4个方面对其复杂性进行分析。描述了区域电力需求的系统构成,分析了区域中长期电力负荷预测的影响因素和复杂性特征,提出区域电力负荷中长期预测复杂性测度模型。%In order to solve the problem of regional and structural fluctuations of the power demand all over China,and maintain the sustainable and healthy development of national and regional economy,this paper made a creative and systematic study on mid-long term forecast of regional power load,and analyzed its complexity from four perspectives.The system structure of regional power demand is described;influencing factors and complex characteristics are analyzed;and the complexity measure model for regional power load mid-long term forecasting is proposed.