WorldWideScience

Sample records for forecasting group electricity

  1. Spatial electric load forecasting

    CERN Document Server

    Willis, H Lee

    2002-01-01

    Spatial Electric Load Forecasting Consumer Demand for Power and ReliabilityCoincidence and Load BehaviorLoad Curve and End-Use ModelingWeather and Electric LoadWeather Design Criteria and Forecast NormalizationSpatial Load Growth BehaviorSpatial Forecast Accuracy and Error MeasuresTrending MethodsSimulation Method: Basic ConceptsA Detailed Look at the Simulation MethodBasics of Computerized SimulationAnalytical Building Blocks for Spatial SimulationAdvanced Elements of Computerized SimulationHybrid Trending-Simulation MethodsAdvanced

  2. 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.

  3. electrical load survey electrical load survey and forecast for a ...

    African Journals Online (AJOL)

    eobe

    1, 2NATIONAL CENTRE FOR HYDROPOWER RESEARCH AND DEV., UNIV. OF ILORIN ... be integrated to meet the present and future energy needs of this are ... paper reports the results of electrical load demand and forecast for Elebu rural community located in Kwara State, ... geothermal and ocean energies [5].

  4. A Statistical Approach for Interval Forecasting of the Electricity Price

    DEFF Research Database (Denmark)

    Zhao, Jun Hua; Dong, Zhao Yang; Xu, Zhao

    2008-01-01

    of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we......Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting...... the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value...

  5. 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.

  6. A hybrid approach for probabilistic forecasting of electricity price

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Wang, Yelei

    2014-01-01

    The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due...... to probabilistic interval forecasts can be of great importance to quantify the uncertainties of potential forecasts, thus effectively supporting the decision making activities against uncertainties and risks ahead. This paper proposes a hybrid approach to construct prediction intervals of MCPs with a two...... electricity price forecasting is proposed in this paper. The effectiveness of the proposed hybrid method has been validated through comprehensive tests using real price data from Australian electricity market....

  7. Forecasting electricity usage using univariate time series models

    Science.gov (United States)

    Hock-Eam, Lim; Chee-Yin, Yip

    2014-12-01

    Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.

  8. Forecasting Electrical Load Using a Multi-time-scale Approach

    OpenAIRE

    RINGWOOD John; Murray, F.T.

    1999-01-01

    This paper describes the application of a multi-time-scale technique to the modelling and forecasting of short-term electrical load. The multi-time-scale technique is based on adjusting the underlying short sampling period forecast time series with specific target points and possible aggregated demand. This allows not only improvement of the short sampling period forecast, but also focuses on weighting the accuracy of the forecast at certain critical points e.g. the ov...

  9. 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...

  10. Daily peak electricity load forecasting in South Africa using a ...

    African Journals Online (AJOL)

    major source of variation in peak demand forecasting and the inclusion of temperature has a significant ... This definition of electrical demand has its weaknesses. Electrical ..... The coefficient of t is positive, showing a positive linear trend.

  11. Impact of festival factor on electric quantity multiplication forecast model

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This research aims to improve the forecasting precision of electric quantity. It is discovered that the total electricity consumption considerably increased during the Spring Festival by the analysis of the electric quantity time series from 2002 to 2007 in Shandong province. The festival factor is ascertained to be one of the important seasonal factors affecting the electric quantity fluctuations, and the multiplication model for forecasting is improved by introducing corresponding variables and parameters...

  12. 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.

  13. Statistical approaches to short-term electricity forecasting

    Science.gov (United States)

    Kellova, Andrea

    The study of the short-term forecasting of electricity demand has played a key role in the economic optimization of the electric energy industry and is essential for power systems planning and operation. In electric energy markets, accurate short-term forecasting of electricity demand is necessary mainly for economic operations. Our focus is directed to the question of electricity demand forecasting in the Czech Republic. Firstly, we describe the current structure and organization of the Czech, as well as the European, electricity market. Secondly, we provide a complex description of the most powerful external factors influencing electricity consumption. The choice of the most appropriate model is conditioned by these electricity demand determining factors. Thirdly, we build up several types of multivariate forecasting models, both linear and nonlinear. These models are, respectively, linear regression models and artificial neural networks. Finally, we compare the forecasting power of both kinds of models using several statistical accuracy measures. Our results suggest that although the electricity demand forecasting in the Czech Republic is for the considered years rather a nonlinear than a linear problem, for practical purposes simple linear models with nonlinear inputs can be adequate. This is confirmed by the values of the empirical loss function applied to the forecasting results.

  14. Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Marin Cerjan

    2014-05-01

    Full Text Available Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

  15. Electricity Price Forecasting Based on AOSVR and Outlier Detection

    Institute of Scientific and Technical Information of China (English)

    Zhou Dianmin; Gao Lin; Gao Feng

    2005-01-01

    Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.

  16. FORECASTING ELECTRICITY PRICES IN DEREGULATED WHOLESALE SPOT ELECTRICITY MARKET - A REVIEW

    Directory of Open Access Journals (Sweden)

    Girish Godekere Panchakshara Murthy,

    2014-01-01

    Full Text Available In the new framework of competitive electricity markets, all power market participants need accurate price forecasting tools. Electricity price forecasts characterize significant information that can help captive power producer, independent power producer, power generation companies, power distribution companies or open access consumers in careful planning of their bidding strategies for maximizing their profits, benefits and utilities from long term, medium term and short term perspective. Short term spot electricity price forecasting techniques are either inspired from electrical engineering literature (i.e. load forecasting or from economics literature (i.e. game theory models and the time-series econometric models. In this study we investigate the emergence of spot electricity markets with particular emphasis on Indian electricity market which has never been done before and review selected finance and econometrics inspired literature and models for forecasting electricity spot prices in deregulated wholesale spot electricity markets.

  17. 101 Modelling and Forecasting Periodic Electric Load for a ...

    African Journals Online (AJOL)

    User

    2012-01-24

    Jan 24, 2012 ... In this work, three models are used to analyze the electric load capacity of a ..... Forecasting electricity prices for a day-ahead pool-based electric energy market. ... Control, Operation and Management, Hong Kong pgs.782–7.

  18. Electric power systems advanced forecasting techniques and optimal generation scheduling

    CERN Document Server

    Catalão, João P S

    2012-01-01

    Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

  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. Forecasting the electricity consumption of the Mexican border states maquiladoras

    Energy Technology Data Exchange (ETDEWEB)

    Flores, C.E.; Phelan, P.E. [Arizona State Univ., Dept. of Mechanical and Aerospace Engineering, Tempe, AZ (United States); Mou, J.-I. [Taiwan Semiconductor Manufacturing Co., Operation Planning Div., Hsin-Chu (Taiwan); Bryan, H. [Arizona State Univ., School of Architecture, Tempe, AZ (United States)

    2004-07-01

    The consumption of electricity by maquiladora industries in the Mexican border states is an important driver for determining future powerplant needs in that area. An industrial electricity forecasting model is developed for the border states' maquiladoras, and the outputs are compared with a reference forecasting model developed for the US industrial sector, for which considerably more data are available. This model enables the prediction of the effect of implementing various energy efficiency measures in the industrial sector. As an illustration, here the impact of implementing energy-efficient lighting and motors in the Mexican border states' maquiladoras was determined to be substantial. Without such energy efficiency measures, electricity consumption for these industries is predicted to rise by 64% from 2001 to 2010, but if these measures are implemented on a gradual basis over the same time period, electricity consumption is forecast to rise by only 36%. (Author)

  1. Application of chaotic ant swarm optimization in electric load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Wei-Chiang [Department of Information Management, Oriental Institute of Technology, 58, Section 2, Sichuan Rd., Panchiao, Taipei County 220 (China)

    2010-10-15

    Support vector regression (SVR) had revealed strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, however, these employed evolutionary algorithms themselves have several drawbacks, such as converging prematurely, reaching slowly the global optimal solution, and trapping into a local optimum. This investigation presents an SVR-based electric load forecasting model that applied a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching its suitable parameters combination. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other alternative methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model. (author)

  2. Aerosols for Concentrating Solar Electricity Production Forecasts: Requirement Quantification and ECMWF/MACC Aerosol Forecast Assessment

    OpenAIRE

    Schroedter-Homscheidt, Marion; Oumbe, Armel; Benedetti, Angela; Morcrette, Jean-Jacques

    2013-01-01

    The potential for transferring a larger share of our energy supply toward renewable energy is a widely discussed goal in society, economics, environment, and climate-related programs. For a larger share of electricity to come from fluctuating solar and wind energy-based electricity, production forecasts are required to ensure successful grid integration. Concentrating solar power holds the potential to make the fluctuating solar electricity a dispatchable resource by using both heat storage s...

  3. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    Energy Technology Data Exchange (ETDEWEB)

    Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2011-05-15

    Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  4. A method for short term electricity spot price forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V. [VTT Energy, Espoo (Finland); Laitinen, E.; Haekli, J. [Vaasa Univ. (Finland); Antila, E. [ABB Transmit Oy (Finland)

    1998-08-01

    In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market

  5. An Electrical Energy Consumption Monitoring and Forecasting System

    Directory of Open Access Journals (Sweden)

    J. L. Rojas-Renteria

    2016-10-01

    Full Text Available Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.

  6. Sharing wind power forecasts in electricity markets: A numerical analysis

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Pinson, Pierre; Kazempour, Jalal

    2016-01-01

    In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day......-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding...... flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making...

  7. Modelling and forecasting electricity price variability

    Energy Technology Data Exchange (ETDEWEB)

    Haugom, Erik

    2012-07-01

    The liberalization of electricity sectors around the world has induced a need for financial electricity markets. This thesis is mainly focused on calculating, modelling, and predicting volatility for financial electricity prices. The four first essays examine the liberalized Nordic electricity market. The purposes in these papers are to describe some stylized properties of high-frequency financial electricity data and to apply models that can explain and predict variation in volatility. The fifth essay examines how information from high-frequency electricity forward contracts can be used in order to improve electricity spot-price volatility predictions. This essay uses data from the Pennsylvania-New Jersey-Maryland wholesale electricity market in the U.S.A. Essay 1 describes some stylized properties of financial high-frequency electricity prices, their returns and volatilities at the Nordic electricity exchange, Nord Pool. The analyses focus on distribution properties, serial correlation, volatility clustering, the influence of extreme events and seasonality in the various measures. The objective of Essay 2 is to calculate, model, and predict realized volatility of financial electricity prices for quarterly and yearly contracts. The total variation is also separated into continuous and jump variation. Various market measures are also included in the models in order potentially to improve volatility predictions. Essay 3 compares day-ahead predictions of Nord Pool financial electricity price volatility obtained from a GARCH approach with those obtained using standard time-series techniques on realized volatility. The performances of a total of eight models (two representing the GARCH family and six representing standard autoregressive models) are compared and evaluated. Essay 4 examines whether predictions of day-ahead and week-ahead volatility can be improved by additionally including volatility and covariance effects from related financial electricity contracts

  8. 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

    Electricity demand forecast is an essential tool for energy management and operation scheduling for electric utilities. In power engineering, medium-term forecasting is defined as the prediction up to 12 months ahead, and commonly is performed considering weather climatology and not actual forecasts. This work aims to analyze the predictability of electricity demand on seasonal time scale, considering seasonal samples, i.e. average on three months. Electricity demand data has been provided by Italian Transmission System Operator for eight different geographical areas, in Fig. 1 for each area is shown the average yearly demand anomaly for each season. This work uses data for each summer during 1990-2010 and all the datasets have been pre-processed to remove trends and reduce the influence of calendar and economic effects. The choice of focusing this research on the summer period is due to the critical peaks of demand that power grid is subject during hot days. Weather data have been included considering observations provided by ECMWF ERA-INTERIM reanalyses. Primitive variables (2-metres temperature, pressure, etc) and derived variables (cooling and heating degree days) have been averaged for summer months. A particular attention has been given to the influence of persistence of positive temperature anomaly and a derived variable which count the number of consecutive days of extreme-days has been used. Electricity demand forecast has been performed using linear and nonlinear regression methods and stepwise model selection procedures have been used to perform a variable selection with respect to performance measures. Significance tests on multiple linear regression showed the importance of cooling degree days during summer in the North-East and South of Italy with an increase of statistical significance after 2003, a result consistent with the diffusion of air condition and ventilation equipment in the last decade. Finally, using seasonal climate forecasts we evaluate

  9. Support vector machine for day ahead electricity price forecasting

    Science.gov (United States)

    Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

    2015-05-01

    Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

  10. Flare forecasting based on sunspot-groups characteristics

    National Research Council Canada - National Science Library

    Contarino, Lidia; Zuccarello, Francesca; Romano, Paolo; Spadaro, Daniele; Guglielmino, Salvatore L; Battiato, Viviana

    2009-01-01

    ... accurate flare forecasting. In order to give a contribution to this aspect, we focused our attention on the characteristics that must be fulfilled by sunspot-groups in order to be flare-productive...

  11. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast. Technical Appendix: Volume 1.

    Energy Technology Data Exchange (ETDEWEB)

    None

    1994-02-01

    This publication documents the load forecast scenarios and assumptions used to prepare BPA's Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.

  12. Load Forecasting in Electric Utility Integrated Resource Planning

    Energy Technology Data Exchange (ETDEWEB)

    Carvallo, Juan Pablo [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Larsen, Peter H. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sanstad, Alan H [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Goldman, Charles A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2017-07-19

    Integrated resource planning (IRP) is a process used by many vertically-integrated U.S. electric utilities to determine least-cost/risk supply and demand-side resources that meet government policy objectives and future obligations to customers and, in many cases, shareholders. Forecasts of energy and peak demand are a critical component of the IRP process. There have been few, if any, quantitative studies of IRP long-run (planning horizons of two decades) load forecast performance and its relationship to resource planning and actual procurement decisions. In this paper, we evaluate load forecasting methods, assumptions, and outcomes for 12 Western U.S. utilities by examining and comparing plans filed in the early 2000s against recent plans, up to year 2014. We find a convergence in the methods and data sources used. We also find that forecasts in more recent IRPs generally took account of new information, but that there continued to be a systematic over-estimation of load growth rates during the period studied. We compare planned and procured resource expansion against customer load and year-to-year load growth rates, but do not find a direct relationship. Load sensitivities performed in resource plans do not appear to be related to later procurement strategies even in the presence of large forecast errors. These findings suggest that resource procurement decisions may be driven by other factors than customer load growth. Our results have important implications for the integrated resource planning process, namely that load forecast accuracy may not be as important for resource procurement as is generally believed, that load forecast sensitivities could be used to improve the procurement process, and that management of load uncertainty should be prioritized over more complex forecasting techniques.

  13. Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg

    2013-01-01

    -varying regression model. In a second step, time-series models, i.e., ARMA and Holt–Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pool's Elspot, during a two......A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time...

  14. A simple forecasting model for industrial electric energy consumption

    Energy Technology Data Exchange (ETDEWEB)

    Al-Shehri, Abdallah [King Fahd Univ. of Petroleum and Minerals, Electrical Engineering Dept., Dhaharan (Saudi Arabia)

    2000-07-01

    A single-equation model is developed and employed for forecasting industrial electric energy consumption in the Saudi Consolidated Electric Company in the Eastern Province (SCECO-East) of Saudi Arabia. SCECO-East's industrial loads are composed mainly of oil-related and petrochemical industries. Even though industrial loads are generally characterised by their steadiness, the harsh weather conditions of the Eastern Province cause great variations in the industrial electric energy consumption at SCECO-East. The developed model reflects these variations. MATLAB is used to solve the model. (Author)

  15. A New Method for Grey Forecasting Model Group

    Institute of Scientific and Technical Information of China (English)

    李峰; 王仲东; 宋中民

    2002-01-01

    In order to describe the characteristics of some systems, such as the process of economic and product forecasting, a lot of discrete data may be used. Although they are discrete, the inside law can be-founded by some methods. For a series that the discrete degree is large and the integrated tendency is ascending, a new method for grey forecasting model group is given by the grey system theory. The method is that it firstly transforms original data, chooses some clique values and divides original data into groups by different clique values; then, it establishes non-equigap GM(1, 1) model for different groups and searches forecasting area of original data by the solution of model. At the end of the paper, the result of reliability of forecasting value is obtained. It is shown that the method is feasible.

  16. SVR-Boosting ensemble model for electricity price forecasting in electric power market

    Institute of Scientific and Technical Information of China (English)

    ZHOU Dian-min; GAO Lin; GUAN Xiao-hong; GAO Feng

    2008-01-01

    A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boos-ting) is presented in this paper for electricity price forecasting in electric power market. In the light of charac-the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accu-racy, and possess comparatively satisfactory generalization capability.

  17. An Electrical Energy Consumption Monitoring and Forecasting System

    OpenAIRE

    Rojas-Renteria, J. L.; T. D. Espinoza-Huerta; F. S. Tovar-Pacheco; Gonzalez-Perez, J. L.; Lozano-Dorantes, R.

    2016-01-01

    Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecastin...

  18. Electricity generation modeling and photovoltaic forecasts in China

    Science.gov (United States)

    Li, Shengnan

    With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979--2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.

  19. An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism

    OpenAIRE

    Jingmin Wang; Jian Zhang; Jing Nie

    2016-01-01

    Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity con...

  20. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Hong-Juan Li

    2013-04-01

    Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

  1. Adaptive load forecasting of the Hellenic electric grid

    Institute of Scientific and Technical Information of China (English)

    S. Sp. PAPPAS; L. EKONOMOU; V. C. MOUSSAS; P. KARAMPELAS; S. K. KATSIKAS

    2008-01-01

    Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique,which will use efficiently the information contained in the available data,is required,so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA),for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load,an anomaly is detected and,furthermore,when the pattern matches a known case of anomaly,the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A.,Athens,Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

  2. Analysis and forecast of electrical distribution system materials. Final report. Volume III. Appendix

    Energy Technology Data Exchange (ETDEWEB)

    Love, C G

    1976-08-23

    These appendixes are referenced in Volume II of this report. They contain the detailed electrical distribution equipment requirements and input material requirements forecasts. Forecasts are given for three electric energy usage scenarios. Also included are data on worldwide reserves and demand for 30 raw materials required for the manufacture of electrical distribution equipment.

  3. Fuzzy forecasting based on fuzzy-trend logical relationship groups.

    Science.gov (United States)

    Chen, Shyi-Ming; Wang, Nai-Yi

    2010-10-01

    In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

  4. Short Term Electrical Load Forecasting by Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hong Li

    2016-07-01

    Full Text Available This paper presents an application of artificial neural networks for short-term times series electrical load forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of training process. Historical data of hourly power load as well as hourly wind power generation are sourced from European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training with the adaptive learning factor starting at different initial value and errors behave volatile with constant learning factors with different values

  5. 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.

  6. 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.

  7. Thirty-Year Solid Waste Generation Forecast by Treatability Group

    Energy Technology Data Exchange (ETDEWEB)

    Thomas, C.

    1994-07-01

    This report is Phase II of the Thirty-Year Solid Waste Generation Forecast for Facilities at SRS. Phase I of the forecast, Thirty-Year Solid Waste Generation Forecast for Facilities at SRS (Reference 4), forecasts the yearly quantities of LLW, hazardous, mixed, and TRU wastes generated over the next 30 years by operations, decontamination and decommissioning, and ER activities at SRS. The solid wastes stored or generated at SRS must be treated and disposed of in accordance with federal, state, and local laws and regulations. To evaluate, select, and justify the use of promising treatment technologies and to evaluate the potential impacts to the environment, the generic waste categories described in the Phase I report must be divided into smaller classifications with similar physical, chemical, and radiological characterisics. These classifications, defined as treatability groups, can be used in the WMEIS process to evaluate treatment options. The methodology used to categorize the SRS Solid Waste Streams into treatability groups is based on the premise that the key information ncessary for identifying like treatments can be discerned from the radiological, physical, and chemical properties of the wastes and their contaminants.

  8. 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.

  9. 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)

  10. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

    Energy Technology Data Exchange (ETDEWEB)

    Tan, Zhongfu; Zhang, Jinliang; Xu, Jun [North China Electric Power University, Beijing 102206 (China); Wang, Jianhui [Argonne National Laboratory, Argonne, IL 60439 (United States)

    2010-11-15

    This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)

  11. Forecasting of Hourly Photovoltaic Energy in Canarian Electrical System

    Science.gov (United States)

    Henriquez, D.; Castaño, C.; Nebot, R.; Piernavieja, G.; Rodriguez, A.

    2010-09-01

    The Canarian Archipelago face similar problems as most insular region lacking of endogenous conventional energy resources and not connected to continental electrical grids. A consequence of the "insular fact" is the existence of isolated electrical systems that are very difficult to interconnect due to the considerable sea depths between the islands. Currently, the Canary Islands have six isolated electrical systems, only one utility generating most of the electricity (burning fuel), a recently arrived TSO (REE) and still a low implementation of Renewable Energy Resources (RES). The low level of RES deployment is a consequence of two main facts: the weakness of the stand-alone grids (from 12 MW in El Hierro up to only 1 GW in Gran Canaria) and the lack of space to install RES systems (more than 50% of the land protected due to environmental reasons). To increase the penetration of renewable energy generation, like solar or wind energy, is necessary to develop tools to manage them. The penetration of non manageable sources into weak grids like the Canarian ones causes a big problem to the grid operator. There are currently 104 MW of PV connected to the islands grids (Dec. 2009) and additional 150 MW under licensing. This power presents a serious challenge for the operation and stability of the electrical system. ITC, together with the local TSO (Red Eléctrica de España, REE) started in 2008 and R&D project to develop a PV energy prediction tool for the six Canarian Insular electrical systems. The objective is to supply reliable information for hourly forecast of the generation dispatch programme and to predict daily solar radiation patterns, in order to help program spinning reserves. ITC has approached the task of weather forecasting using different numerical model (MM5 and WRF) in combination with MSG (Meteosat Second Generation) images. From the online data recorded at several monitored PV plants and meteorological stations, PV nominal power and energy produced

  12. Advances in electric power and energy systems load and price forecasting

    CERN Document Server

    2017-01-01

    A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

  13. The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang

    Directory of Open Access Journals (Sweden)

    Gayatri Dwi Santika

    2017-03-01

    Full Text Available This study applied Fuzzy Inference System Sugeno to forecast electrical load by considering the external factors. To see the accuracy of forecasting using Fuzzy Inference System Sugeno, then a comparison between the forecasting results of Fuzzy Inference System Sugeno using historical data with Fuzzy Inference System Sugeno using external factors was done. By using external factors method, resulted the smallest RMSE of 0762 and using historical data obtained error (RMSE of 1028. The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy Inference System Sugeno using only historical data.

  14. Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

    OpenAIRE

    Xunlin Jiang; Haifeng Ling; Jun Yan; Bo Li; Zhao Li

    2013-01-01

    Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, ...

  15. Dynamical behavior of price forecasting in structures of group correlations

    Science.gov (United States)

    Lim, Kyuseong; Kim, Soo Yong; Kim, Kyungsik

    2015-07-01

    We investigate the prediction of the future prices from the structures and the networks of the companies in special financial groups. After the financial group network has been constructed from the value of the high cross-correlation, each company in a group is simulated and analyzed how it buys or sells stock is anaylzed and how it makes rational investments is forecasted. In the shortmemory behavior rather than the long-memory behavior, each company among a group can make a rational investment decision by using a stochastic evolution rule in the financial network. In particular, we simulate and analyze the investment situation in connection with the empirical data and the simulated result.

  16. Analytical Treatment of Forecasts of Electric Energy Consumption in Latvia

    Science.gov (United States)

    Balodis, M.; Gavars, V.; Andersons, J.

    2014-06-01

    In the paper, the changes in electric energy consumption are analyzed as associated with structural changes in the Latvian economy of postsocialistic period. To the analysis, a particular approach is applied, which consists in comparison of the basic and specific electricity consumption indices in West-, Central-, and East-European states for the time span of 1990-2010, with differences and tendencies of changes revealed. Tendencies of the type are determined for the electric energy consumption in Latvia, and recommendations are given for the use of such indices in the relevant forecasts. Rakstā apskatītas elektroenerģijas patēriņa izmaiņas, kas saistītas ar Latvijas postsociālisma perioda ekonomikas strukturālām izmaiņām. Rakstā dota Latvijas galveno elektroenerģijas patēriņa indikatoru analīze, lietojot īpašu pieeju - Rietumeiropas, Centrāleiropas un Austrumeiropas valstu indikatoru salīdzinājumu. Analizēts periods no 1990. gada līdz 2010. gadam. Salīdzināti Eiropas valstu grupu īpatnējie elektroenerģijas patēriņa indikatori un noskaidrotas to atšķirības un izmaiņu tendences. Noteiktas elektroenerģijas patēriņa izmaiņu tendences Latvijā. Dotas rekomendācijas par šo indikatoru izmantošanu elektroenerģijas patēriņa prognozēšanā. 07.05.2014.

  17. Electrical consumption forecast using actual data of building end-use decomposition

    OpenAIRE

    Escrivá Escrivá, Guillermo; Roldán Blay, Carlos; Álvarez Bel, Carlos María

    2014-01-01

    The calculation of electricity consumption forecast a few days ahead is a complex issue and studies about this matter are continually being performed. Advances in this field enable obtaining consumption forecasts increasingly accurate. These consumption forecasts aim to improve the knowledge of the facilities, the planning and control of consumption and the measurement and verification of energy saving measures, among others. In this study the authors present several advances related to consu...

  18. 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...

  19. An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism

    Directory of Open Access Journals (Sweden)

    Jingmin Wang

    2016-01-01

    Full Text Available Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity consumption forecast as a difficult study influenced by various factors. This paper proposed an improved Artificial Bee Colony (ABC algorithm which combined with multivariate linear regression (MLR for exploring the influencing mechanism of various factors on Chinese electricity consumption and forecasting electricity consumption in the future. The results indicated that the improved ABC algorithm in view of the various factors is superior to traditional models just considering unilateralism in accuracy and persuasion. The overall findings cast light on this model which provides a new scientific and effective way to forecast the medium and long term electricity consumption.

  20. 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.

  1. The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang

    National Research Council Canada - National Science Library

    Gayatri Dwi Santika; wayan f mahmudy

    2017-01-01

    .... The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy...

  2. Verification of ECMWF and ECMWF/MACC's global and direct irradiance forecasts with respect to solar electricity production forecasts

    Directory of Open Access Journals (Sweden)

    M. Schroedter-Homscheidt

    2017-02-01

    Full Text Available The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI. The European Centre for Medium-Range Weather Forecasts (ECMWF has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998 global to direct irradiance

  3. Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Kazempour, Jalal; Pinson, Pierre

    2017-01-01

    by offering better knowledge of the market operation, leading subsequently to a more competitive energy market. Driven by the above regulation, we consider an equilibrium study to address how public information of aggregate wind power forecasts can potentially affect market results, social welfare as well......Following a call to foster a transparent and more competitive market, member states of the European transmission system operator are required to publish, among other information, aggregate wind power forecasts. The publication of the latter information is expected to benefit market participants......-theoretic approach (diagonalization) is incorporated in order to investigate the existence of an equilibrium for various values of aggregate forecast. As anticipated, variations in public forecasts will affect market results and, more precisely, under-forecasts can mislead power producers to make decisions...

  4. Electricity forecasting on the individual household level enhanced based on activity patterns.

    Science.gov (United States)

    Gajowniczek, Krzysztof; Ząbkowski, Tomasz

    2017-01-01

    Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents' daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.

  5. Is Urbanisation Rate a Feasible Supplemental Parameter in Forecasting Electricity Consumption in China?

    Directory of Open Access Journals (Sweden)

    Biao Yang

    2016-01-01

    Full Text Available Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh.

  6. Electricity price forecasting using generalized regression neural network based on principal components analysis

    Institute of Scientific and Technical Information of China (English)

    牛东晓; 刘达; 邢棉

    2008-01-01

    A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.

  7. Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

    Directory of Open Access Journals (Sweden)

    Yuancheng Li

    2016-11-01

    Full Text Available The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays. Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

  8. 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

  9. Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Xunlin Jiang

    2013-01-01

    Full Text Available Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN and particle swarm optimization (PSO. A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data.

  10. 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.

  11. Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market

    Directory of Open Access Journals (Sweden)

    Chuntian Cheng

    2016-10-01

    Full Text Available For the power systems, for which few data are available for mid-term electricity market clearing price (MCP forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N model is proposed in this paper. Over the traditional GM(0, N model, three major improvements of the proposed model are: (i the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii the model parameters are identified by an improved particle swarm optimization (PSO instead of the least square method (LSM for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR model, the traditional GM(0, N model and the artificial neural network (ANN model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market.

  12. Forecasting electricity spot market prices with a k-factor GIGARCH process

    Energy Technology Data Exchange (ETDEWEB)

    Diongue, Abdou Ka [Universite Gaston Berger de Saint-Louis, UFR SAT, BP 234, Saint-Louis Senegal and Research Fellow at Queensland University of Technology, 2 George Street, GPO Box 2434, Brisbane QLD 4001 (Australia); Guegan, Dominique [Paris School of economics, CES-MSE, Universite Paris1 Pantheon-Sorbonne, 106 boulevard de l' Hopital, 75647 Paris, Cedex 13 (France); Vignal, Bertrand [Ingenieur EDF R and D, 1 avenue du general de Gaulle, 92141 Clamart cedex (France)

    2009-04-15

    In this article, we investigate conditional mean and conditional variance forecasts using a dynamic model following a k-factor GIGARCH process. Particularly, we provide the analytical expression of the conditional variance of the prediction error. We apply this method to the German electricity price market for the period August 15, 2000-December 31, 2002 and we test spot prices forecasts until one-month ahead forecast. The forecasting performance of the model is compared with a SARIMA-GARCH benchmark model using the year 2003 as the out-of-sample. The proposed model outperforms clearly the benchmark model. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria. (author)

  13. 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.

  14. Long-term forecasting of hourly electricity load: Identification of consumption profiles and segmentation of customers

    DEFF Research Database (Denmark)

    Møller Andersen, Frits; Larsen, Helge V.; Boomsma, Trine Krogh

    2013-01-01

    Data for aggregated hourly electricity demand shows systematic variations over the day, week, and seasons, and forecasting of aggregated hourly electricity load has been the subject of many studies. With hourly metering of individual customers, data for individual consumption profiles is availabl...... as the shares of consumption by categories of customers change and new consumption technologies such as electrical vehicles and (for Denmark in particular) individual heat pumps are introduced. © 2013 Elsevier Ltd. All rights reserved.......Data for aggregated hourly electricity demand shows systematic variations over the day, week, and seasons, and forecasting of aggregated hourly electricity load has been the subject of many studies. With hourly metering of individual customers, data for individual consumption profiles is available....... Using this data and analysing the case of Denmark, we show that consumption profiles for categories of customers are equally systematic but very different for distinct categories, that is, distinct categories of customers contribute differently to the aggregated electricity load profile. Therefore...

  15. Forecasting of the electrical actuators condition using stator’s current signals

    Science.gov (United States)

    Kruglova, T. N.; Yaroshenko, I. V.; Rabotalov, N. N.; Melnikov, M. A.

    2017-02-01

    This article describes a forecasting method for electrical actuators realized through the combination of Fourier transformation and neural network techniques. The method allows finding the value of diagnostic functions in the iterating operating cycle and the number of operational cycles in time before the BLDC actuator fails. For forecasting of the condition of the actuator, we propose a hierarchical structure of the neural network aiming to reduce the training time of the neural network and improve estimation accuracy.

  16. Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2015-09-01

    Full Text Available This paper presents the analysis of the importance of a set of explanatory (input variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL. The available input variables include extensive hourly time series records of weather forecasts, previous prices, and regional aggregation of power generations and power demands. The paper presents the comparisons of the forecasting results achieved with a model which includes all these available input variables (EMPF model with respect to those obtained by other forecasting models containing a reduced set of input variables. These comparisons identify the most important variables for forecasting purposes. In addition, a novel Reference Explanatory Model for Price Estimations (REMPE that achieves hourly price estimations by using actual power generations and power demands of such day is described in the paper, which offers the lowest limit for the forecasting error of the EMPF model. All the models have been implemented using the same technique (artificial neural networks and have been satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. The relative importance of each explanatory variable is identified for the day-ahead price forecasts in the MIBEL. The comparisons also allow outlining guidelines of the value of the different types of input information.

  17. Load forecast method of electric vehicle charging station using SVR based on GA-PSO

    Science.gov (United States)

    Lu, Kuan; Sun, Wenxue; Ma, Changhui; Yang, Shenquan; Zhu, Zijian; Zhao, Pengfei; Zhao, Xin; Xu, Nan

    2017-06-01

    This paper presents a Support Vector Regression (SVR) method for electric vehicle (EV) charging station load forecast based on genetic algorithm (GA) and particle swarm optimization (PSO). Fuzzy C-Means (FCM) clustering is used to establish similar day samples. GA is used for global parameter searching and PSO is used for a more accurately local searching. Load forecast is then regressed using SVR. The practical load data of an EV charging station were taken to illustrate the proposed method. The result indicates an obvious improvement in the forecasting accuracy compared with SVRs based on PSO and GA exclusively.

  18. Modeling and forecasting electricity price jumps in the Nord Pool power market

    DEFF Research Database (Denmark)

    Knapik, Oskar

    extreme prices and forecasting of the price jumps is crucial for risk management and market design. In this paper, we consider the problem of the impact of fundamental price drivers on forecasting of price jumps in NordPool intraday market. We develop categorical time series models which take into account...... i) price drivers, ii) persistence, iii) seasonality of electricity prices. The models are shown to outperform commonly-used benchmark. The paper shows how crucial for price jumps forecasting is to incorporate additional knowledge on price drivers like loads, temperature and water reservoir level...

  19. A New Two-Stage Approach to Short Term Electrical Load Forecasting

    Directory of Open Access Journals (Sweden)

    Dragan Tasić

    2013-04-01

    Full Text Available In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach.

  20. Short Term Price Forecasting in Electricity Market Considering the Effect of Wind Units\\\\\\' Generation

    Directory of Open Access Journals (Sweden)

    H. Taherian

    2014-05-01

    Full Text Available The price signal in a competitive electricity market has a major importance in all planning and commissioning activities. Also, the electricity price has a non-deterministic nature and is affected by various parameters in short and long terms. Active players in electricity market need accurate and effective price forecasting for risk management. With the increased use of renewable energies, especially wind energy, the electricity price is being affected by this new parameter, as the intermittent nature of wind generation has further complicated the process of instantaneous balancing of power system demand against power generation. In this paper, using the Nord Pool electricity market data, the effect of wind units' generation on price forecasting is studied. The main idea is based on presenting an intelligent model for forecasting the Market Clearing Price through the use of a multilayer perceptron neural network based on hybrid genetic model and Imperialist Competitive algorithm. This hybrid model has a better accuracy, compared to the conventional neural networks (based on gradient-based optimization algorithms, and has the ability of converging towards the absolute optimum. The results verify the high accuracy of this model in short term electricity price forecasting.

  1. Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Li-Ling Peng

    2016-03-01

    Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD method and quantum particle swarm optimization algorithm (QPSO for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia market and the New York Independent System Operator (NYISO, New York, USA are used for comparing the forecasting performances of different forecasting models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

  2. 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)

  3. Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results). Executive summary. [weather forecasting

    Science.gov (United States)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions so as to significantly reduce the cost for frost and freeze protection and crop losses. The design and implementation of the first phase of an economic experiment which will monitor citrus growers decisions, actions, costs and losses, and meteorological forecasts and actual weather events was carried out. The economic experiment was designed to measure the change in annual protection costs and crop losses which are the direct result of improved temperature forecasts. To estimate the benefits that may result from improved temperature forecasting capability, control and test groups were established with effective separation being accomplished temporally. The control group, utilizing current forecasting capability, was observed during the 1976-77 frost season and the results are reported. A brief overview is given of the economic experiment, the results obtained to date, and the work which still remains to be done.

  4. A regime-switching stochastic volatility model for forecasting electricity prices

    DEFF Research Database (Denmark)

    Exterkate, Peter; Knapik, Oskar

    In a recent review paper, Weron (2014) pinpoints several crucial challenges outstanding in the area of electricity price forecasting. This research attempts to address all of them by i) showing the importance of considering fundamental price drivers in modeling, ii) developing new techniques...... for probabilistic (i.e. interval or density) forecasting of electricity prices, iii) introducing an universal technique for model comparison. We propose new regime-switching stochastic volatility model with three regimes (negative jump, normal price, positive jump (spike)) where the transition matrix depends...... on explanatory variables. Bayesian inference is explored in order to obtain predictive densities. The main focus of the paper is on shorttime density forecasting in Nord Pool intraday market. We show that the proposed model outperforms several benchmark models at this task....

  5. Research in Residential Electricity Characteristics and Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Haixia Feng

    2013-07-01

    Full Text Available In this paper we make research in Residential short-term load forecasting. Different application scenes have different affecting factors of short-term load, so we should specifically analysis of factors that affect the load of the residential electricity. We use SPSS (Statistic Package for Social Science to figure out the relationship between the daily load and temperature, weather conditions and other factors, finding the main factors among the impacting factors, and analyzing residential electricity consumption habits and load characteristics. Then, the paper introduces the common prediction methods. Combining with the above analysis to choose short-term load forecasting methods for residential users, we create automatic linear regression model and artificial neural network model to predict the future electricity load, calculating the residual between the predicted values and the actual values and mean square deviation of the values, and evaluating the accuracy of the load forecasting. The results prove that automatic linear regression model is effective in residential short-term electricity load forecasting.

  6. An Electrical Energy Consumption Monitoring and Forecasting System

    National Research Council Canada - National Science Library

    J. L. Rojas-Renteria; T. D. Espinoza-Huerta; F. S. Tovar-Pacheco; J. L. Gonzalez-Perez; R. Lozano-Dorantes

    2016-01-01

    Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations...

  7. Performance of fuzzy approach in Malaysia short-term electricity load forecasting

    Science.gov (United States)

    Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee

    2014-12-01

    Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in forecasting electricity load when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.

  8. 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.

  9. 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

  10. Electricity spot price forecasting in free power market

    Energy Technology Data Exchange (ETDEWEB)

    Lilleberg, J.; Laitinen, E.K. [Vaasa Univ. (Finland)

    1998-08-01

    Deregulation has brought many changes to the electricity market. Freedom of choice has been granted to both the consumers and the utilities. Consumers may choose the seller of their energy. Utilities have a wider array of sources to acquire their electricity from. Also the types of sales contracts used are changing to fill the needs of this new situation. The consumers` right to choose has introduced a new risk uncertainty of volume, which was not true during the times of monopoly. As sold volume is unsure and the energy is not sold on same terms as it is bought, a price risk has to be dealt with also. The electric utility has to realize this, select a risk level that suits its business strategy and optimize its actions according to the selected risk level. The number of participants will grow as the electricity market integrates into a common market for Scandinavia and even Europe. Big customers are also taking a more active role in the market, further increasing the number of participants. This makes old bilateral arrangements outdated. New tools are needed to control the new business environment. The goal of this project has been to develop a theoretical model to predict the price in the Finnish electricity exchange, El-Ex Oy. An extensive literature review was conducted in order to (1) examine the solutions in deregulation of electricity markets in other countries, esp. in Norway and UK, (2) find similarities and differences in electricity exchange and exchanges generally and (3) find major sources of problems and inefficiency in the market

  11. Electricity Consumption Forecasting in the Age of Big Data

    Directory of Open Access Journals (Sweden)

    Xiaojia Wang

    2013-09-01

    Full Text Available In the age of big data, information mining technology has undergone tremendous change; traditional forecasting mining technology has not been able to solve the information mining problems under a large scale of data. this paper put forward a modeling mechanism of information analysis and mining under the age of big data, the modeling mechanism is, first, construct the model of task decomposition of information by MapReduce tool, then, make data preprocessing and mining operation according to the single task data sheet, use mathematical model, artificial intelligence and other methods to construct the new ideas of information analysis and data mining under the age of big data, finally, a case study presented to demonstrate the feasibility and rationality of our approach.

  12. 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)

  13. The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO weight-determined combination models.” These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.

  14. Using Information Processing Techniques to Forecast, Schedule, and Deliver Sustainable Energy to Electric Vehicles

    Science.gov (United States)

    Pulusani, Praneeth R.

    As the number of electric vehicles on the road increases, current power grid infrastructure will not be able to handle the additional load. Some approaches in the area of Smart Grid research attempt to mitigate this, but those approaches alone will not be sufficient. Those approaches and traditional solution of increased power production can result in an insufficient and imbalanced power grid. It can lead to transformer blowouts, blackouts and blown fuses, etc. The proposed solution will supplement the ``Smart Grid'' to create a more sustainable power grid. To solve or mitigate the magnitude of the problem, measures can be taken that depend on weather forecast models. For instance, wind and solar forecasts can be used to create first order Markov chain models that will help predict the availability of additional power at certain times. These models will be used in conjunction with the information processing layer and bidirectional signal processing components of electric vehicle charging systems, to schedule the amount of energy transferred per time interval at various times. The research was divided into three distinct components: (1) Renewable Energy Supply Forecast Model, (2) Energy Demand Forecast from PEVs, and (3) Renewable Energy Resource Estimation. For the first component, power data from a local wind turbine, and weather forecast data from NOAA were used to develop a wind energy forecast model, using a first order Markov chain model as the foundation. In the second component, additional macro energy demand from PEVs in the Greater Rochester Area was forecasted by simulating concurrent driving routes. In the third component, historical data from renewable energy sources was analyzed to estimate the renewable resources needed to offset the energy demand from PEVs. The results from these models and components can be used in the smart grid applications for scheduling and delivering energy. Several solutions are discussed to mitigate the problem of overloading

  15. Controlling Electricity Consumption by Forecasting its Response to Varying Prices

    DEFF Research Database (Denmark)

    Corradi, Olivier; Ochsenfeld, Henning Peter; Madsen, Henrik

    2013-01-01

    electricity consumption using a one-way price signal. Estimation of the price-response is based on data measurable at grid level, removing the need to install sensors and communication devices between each individual consumer and the price-generating entity. An application for price-responsive heating systems......In a real-time electricity pricing context where consumers are sensitive to varying prices, having the ability to anticipate their response to a price change is valuable. This paper proposes models for the dynamics of such price-response, and shows how these dynamics can be used to control...

  16. Electricity Price Forecasting using Sale and Purchase Curves: The X-Model

    OpenAIRE

    Florian Ziel; Rick Steinert

    2015-01-01

    Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usually done in time series or data mining approaches, we model and utilize its true source: the sale and purchase curves of the electricity exchange. We will refer to this new model ...

  17. 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.

  18. Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting

    Directory of Open Access Journals (Sweden)

    Nenad Floranović

    2013-02-01

    Full Text Available Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI has been recently proposed in regression tasks, mostly for feature selection and for identifying the real instances from training sets that contains noise and outliers. This paper proposes a methodology for the training set selection in a least squares support vector machines (LS-SVMs load forecasting model. A new application of the concept of MI is presented for the selection of a training set based on MI computation between initial training set instances and testing set instances. Accordingly, several LS-SVMs models have been trained, based on the proposed methodology, for hourly prediction of electric load for one day ahead. The results obtained from a real-world data set indicate that the proposed method increases the accuracy of load forecasting as well as reduces the size of the initial training set needed for model training.

  19. Hybrid partial least squares and neural network approach for short-term electrical load forecasting

    Institute of Scientific and Technical Information of China (English)

    Shukang YANG; Ming LU; Huifeng XUE

    2008-01-01

    Intelligent systems and methods such as the neural network (NN) are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares (PLS) and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.

  20. Day-Ahead Short-Term Forecasting Electricity Load via Approximation

    Science.gov (United States)

    Khamitov, R. N.; Gritsay, A. S.; Tyunkov, D. A.; E Sinitsin, G.

    2017-04-01

    The method of short-term forecasting of a power consumption which can be applied to short-term forecasting of power consumption is offered. The offered model is based on sinusoidal function for the description of day and night cycles of power consumption. Function coefficients - the period and amplitude are set up is adaptive, considering dynamics of power consumption with use of an artificial neural network. The presented results are tested on real retrospective data of power supply company. The offered method can be especially useful if there are no opportunities of collection of interval indications of metering devices of consumers, and the power supply company operates with electrical supply points. The offered method can be used by any power supply company upon purchase of the electric power in the wholesale market. For this purpose, it is necessary to receive coefficients of approximation of sinusoidal function and to have retrospective data on power consumption on an interval not less than one year.

  1. Tourism forecasting using modified empirical mode decomposition and group method of data handling

    Science.gov (United States)

    Yahya, N. A.; Samsudin, R.; Shabri, A.

    2017-09-01

    In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.

  2. Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

    Science.gov (United States)

    Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud

    1996-01-01

    The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the

  3. Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results). [weather forecasting

    Science.gov (United States)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions. An economic experiment was carried out which will monitor citrus growers' decisions, actions, costs and losses, and meteorological forecasts and actual weather events and will establish the economic benefits of improved temperature forecasts. A summary is given of the economic experiment, the results obtained to date, and the work which still remains to be done. Specifically, the experiment design is described in detail as are the developed data collection methodology and procedures, sampling plan, data reduction techniques, cost and loss models, establishment of frost severity measures, data obtained from citrus growers, National Weather Service, and Federal Crop Insurance Corp., resulting protection costs and crop losses for the control group sample, extrapolation of results of control group to the Florida citrus industry and the method for normalization of these results to a normal or average frost season so that results may be compared with anticipated similar results from test group measurements.

  4. Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression

    KAUST Repository

    Taieb, Souhaib Ben

    2016-03-02

    Smart electricity meters are currently deployed in millions of households to collect detailed individual electricity consumption data. Compared with traditional electricity data based on aggregated consumption, smart meter data are much more volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand in order to undertake appropriate planning of generation and distribution. We propose to estimate an additive quantile regression model for a set of quantiles of the future distribution using a boosting procedure. By doing so, we can benefit from flexible and interpretable models, which include an automatic variable selection. We compare our approach with three benchmark methods on both aggregated and disaggregated scales using a smart meter data set collected from 3639 households in Ireland at 30-min intervals over a period of 1.5 years. The empirical results demonstrate that our approach based on quantile regression provides better forecast accuracy for disaggregated demand, while the traditional approach based on a normality assumption (possibly after an appropriate Box-Cox transformation) is a better approximation for aggregated demand. These results are particularly useful since more energy data will become available at the disaggregated level in the future.

  5. 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.

  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. Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

    Science.gov (United States)

    Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

    2016-01-01

    Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

  8. FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL

    Directory of Open Access Journals (Sweden)

    Joao Bosco de Castro

    2010-11-01

    Full Text Available This work aims to propose the application of the ARX model to forecast residential electricity consumption in Brazil. Such estimates are critical for decision making in the energy sector,  from a technical, economic and environmentally sustainable standpoint. The demand for electricity follows a multiplicative model based on economic theory and involves four explanatory variables: the cost of residential electricity, the actual average income, the inflation of domestic utilities and the electricity consumption. The coefficients of the electricity consumption equation  were determined using the ARX model, which considers the influence of exogenous variables to estimate the dependent variable and employs an autoregression process for residual modeling to improve the explanatory power. The resulting model has a determination coefficient of 95.4 percent and all estimated coefficients were significant at the 0.10 descriptive level. Residential electricity consumption estimates were also determined for January and February 2010 within the 95 percent confidence interval, which included the actual consumption figures observed. The proposed model has been shown to be useful for estimating residential electricity consumption  in Brazil. Key-words: Time series. Electricity consumption. ARX modeling. 

  9. 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

  10. Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

    Directory of Open Access Journals (Sweden)

    Yunxuan Dong

    2017-04-01

    Full Text Available The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

  11. Using GM (1,1 Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

    Directory of Open Access Journals (Sweden)

    Huiru Zhao

    2016-01-01

    Full Text Available Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1 (GM (1,1, optimized by moth-flame optimization (MFO algorithm with rolling mechanism (Rolling-MFO-GM (1,1, was put forward. The parameters a and b of GM (1,1 were optimized by employing moth-flame optimization algorithm (MFO, which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1 for annual electricity consumption prediction when compared with least square regression (LSR, GM (1,1, FOA (fruit fly optimization-GM (1,1, MFO-GM (1,1, Rolling-LSR, Rolling-GM (1,1 and Rolling-FOA-GM (1,1. The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.

  12. 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.

  13. A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

    Directory of Open Access Journals (Sweden)

    Francisco Martínez-Álvarez

    2015-11-01

    Full Text Available Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i to provide a compact mathematical formulation of the mainly used techniques; (ii to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

  14. A RBF neural network model with GARCH errors: Application to electricity price forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Coelho, Leandro dos Santos [Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Santos, Andre A.P. [Department of Statistics, Universidad Carlos III de Madrid, C/ Madrid, 126, 28903 Getafe, Madrid (Spain)

    2011-01-15

    In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose an estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices. (author)

  15. Matlab for Forecasting of Electric Power Load Based on BP Neural Network

    Science.gov (United States)

    Wang, Xi-Ping; Shi, Ming-Xi

    Modeling and predicting electricity consumption play a vital role both in developed and developing countries for policy makers and related organizations. Improve load forecasting technology level is not only beneficial to plan power management and make reasonable construction plan, but also good for saving energy and reducing power cost, and then, it can improve the economic benefits and social benefit for power system. BP neural network is one of the most widely used neural networks and it has many advantages in the power load forecasting. Matlab has become the best technology application software which has been internationally recognized, the software has many characteristics, such as data visualization function and neural network toolbox, for these, it is the essential software when we do some research on neural network.

  16. Forecasting and decision-making in electricity markets with focus on wind energy

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi

    This thesis deals with analysis, forecasting and decision making in liberalised electricity markets. Particular focus is on wind power, its interaction with the market and the daily decision making of wind power generators. Among recently emerged renewable energy generation technologies, wind power...... has become the global leader in terms of installed capacity and advancement. This makes wind power an ideal candidate to analyse the impact of growing renewable energy generation capacity on the electricity markets. Furthermore, its present status of a significant supplier of electricity makes...... derivation of practically applicable tools for decision making highly relevant. The main characteristics of wind power differ fundamentally from those of conventional thermal power. Its effective generation capacity varies over time and is directly dependent on the weather. This dependency makes future...

  17. 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...

  18. 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.

  19. Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Da Liu

    2013-01-01

    Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.

  20. Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

    Directory of Open Access Journals (Sweden)

    Jaime Buitrago

    2017-01-01

    Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.

  1. A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2015-01-01

    Full Text Available The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

  2. Short-Term Electrical Load Forecasting using Neuro-Fuzzy Models

    Energy Technology Data Exchange (ETDEWEB)

    Park, Young Jin; Shim, Hyun Jeong; Wang, Bo Hyeun [Kang Nung National University (Korea)

    2000-03-01

    This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models, The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptions, radial basis function networks, and neuro-fuzzy models without the structure learning. (author). 23 refs., 11 figs., 8 tabs.

  3. A distributed big data storage and data mining framework for solar-generated electricity quantity forecasting

    Science.gov (United States)

    Wang, Jianzong; Chen, Yanjun; Hua, Rui; Wang, Peng; Fu, Jia

    2012-02-01

    Photovoltaic is a method of generating electrical power by converting solar radiation into direct current electricity using semiconductors that exhibit the photovoltaic effect. Photovoltaic power generation employs solar panels composed of a number of solar cells containing a photovoltaic material. Due to the growing demand for renewable energy sources, the manufacturing of solar cells and photovoltaic arrays has advanced considerably in recent years. Solar photovoltaics are growing rapidly, albeit from a small base, to a total global capacity of 40,000 MW at the end of 2010. More than 100 countries use solar photovoltaics. Driven by advances in technology and increases in manufacturing scale and sophistication, the cost of photovoltaic has declined steadily since the first solar cells were manufactured. Net metering and financial incentives, such as preferential feed-in tariffs for solar-generated electricity; have supported solar photovoltaics installations in many countries. However, the power that generated by solar photovoltaics is affected by the weather and other natural factors dramatically. To predict the photovoltaic energy accurately is of importance for the entire power intelligent dispatch in order to reduce the energy dissipation and maintain the security of power grid. In this paper, we have proposed a big data system--the Solar Photovoltaic Power Forecasting System, called SPPFS to calculate and predict the power according the real-time conditions. In this system, we utilized the distributed mixed database to speed up the rate of collecting, storing and analysis the meteorological data. In order to improve the accuracy of power prediction, the given neural network algorithm has been imported into SPPFS.By adopting abundant experiments, we shows that the framework can provide higher forecast accuracy-error rate less than 15% and obtain low latency of computing by deploying the mixed distributed database architecture for solar-generated electricity.

  4. Development of an Operation Control System for Photovoltaics and Electric Storage Heaters for Houses Based on Information in Weather Forecasts

    Science.gov (United States)

    Obara, Shin'ya

    An all-electric home using an electric storage heater with safety and cleaning is expanded. However, the general electric storage heater leads to an unpleasant room temperature and energy loss by the overs and shorts of the amount of heat radiation when the climate condition changes greatly. Consequently, the operation of the electric storage heater introduced into an all-electric home, a storage type electric water heater, and photovoltaics was planned using weather forecast information distributed by a communication line. The comfortable evaluation (the difference between a room-temperature target and a room-temperature result) when the proposed system was employed based on the operation planning, purchase electric energy, and capacity of photovoltaics was investigated. As a result, comfortable heating operation was realized by using weather forecast data; furthermore, it is expected that the purchase cost of the commercial power in daytime can be reduced by introducing photovoltaics. Moreover, when the capacity of the photovoltaics was increased, the surplus power was stored in the electric storage heater, but an extremely unpleasant room temperature was not shown in the investigation ranges of this paper. By obtaining weather information from the forecast of the day from an external service using a communication line, the heating system of the all-electric home with low energy loss and comfort temperature is realizable.

  5. Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

    Science.gov (United States)

    Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt

    2009-01-01

    The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

  6. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market.

    Science.gov (United States)

    Bozkurt, Ömer Özgür; Biricik, Göksel; Tayşi, Ziya Cihan

    2017-01-01

    Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.

  7. A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

    Directory of Open Access Journals (Sweden)

    Farshid Keynia

    2011-03-01

    Full Text Available Short-term load forecast (STLF is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.

  8. Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

    Science.gov (United States)

    Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

    2017-01-01

    The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

  9. Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Bao Wang

    2012-11-01

    Full Text Available The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA, generalized regression neural network (GRNN and regression model.

  10. A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2016-08-01

    Full Text Available The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist in the literature, there have been few investigations on improving the forecasting effectiveness of electricity price from the perspective of reducing the volatility of data with satisfactory accuracy. Based on reducing the volatility of the electricity price and the forecasting nature of the radial basis function network (RBFN, this paper successfully develops a two-stage model to forecast the day-ahead electricity price, of which the first stage is particle swarm optimization (PSO-core mapping (CM with self-organizing-map and fuzzy set (PCMwSF, and the second stage is selection rule (SR. The PCMwSF stage applies CM, fuzzy set and optimized weights to obtain the future price, and the SR stage is inspired by the forecasting nature of RBFN and effectively selects the best forecast during the test period. The proposed model, i.e., CM-PCMwSF-SR, not only overcomes the difficulty of reducing the high volatility of the electricity price but also leads to a superior forecasting effectiveness than benchmarks.

  11. 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.

  12. The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Qin; Wu, Hongyu; Florita, Anthony R.; Brancucci Martinez-Anido, Carlo; Hodge, Bri-Mathias

    2016-12-01

    The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was compared through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Finally, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.

  13. Ionosphere monitoring and forecast activities within the IAG working group "Ionosphere Prediction"

    Science.gov (United States)

    Hoque, Mainul; Garcia-Rigo, Alberto; Erdogan, Eren; Cueto Santamaría, Marta; Jakowski, Norbert; Berdermann, Jens; Hernandez-Pajares, Manuel; Schmidt, Michael; Wilken, Volker

    2017-04-01

    Ionospheric disturbances can affect technologies in space and on Earth disrupting satellite and airline operations, communications networks, navigation systems. As the world becomes ever more dependent on these technologies, ionospheric disturbances as part of space weather pose an increasing risk to the economic vitality and national security. Therefore, having the knowledge of ionospheric state in advance during space weather events is becoming more and more important. To promote scientific cooperation we recently formed a Working Group (WG) called "Ionosphere Predictions" within the International Association of Geodesy (IAG) under Sub-Commission 4.3 "Atmosphere Remote Sensing" of the Commission 4 "Positioning and Applications". The general objective of the WG is to promote the development of ionosphere prediction algorithm/models based on the dependence of ionospheric characteristics on solar and magnetic conditions combining data from different sensors to improve the spatial and temporal resolution and sensitivity taking advantage of different sounding geometries and latency. Our presented work enables the possibility to compare total electron content (TEC) prediction approaches/results from different centers contributing to this WG such as German Aerospace Center (DLR), Universitat Politècnica de Catalunya (UPC), Technische Universität München (TUM) and GMV. DLR developed a model-assisted TEC forecast algorithm taking benefit from actual trends of the TEC behavior at each grid point. Since during perturbations, characterized by large TEC fluctuations or ionization fronts, this approach may fail, the trend information is merged with the current background model which provides a stable climatological TEC behavior. The presented solution is a first step to regularly provide forecasted TEC services via SWACI/IMPC by DLR. UPC forecast model is based on applying linear regression to a temporal window of TEC maps in the Discrete Cosine Transform (DCT) domain

  14. 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.

  15. An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2014-01-01

    Full Text Available With rapid economic growth, electricity demand is clearly increasing. It is difficult to store electricity for future use; thus, the electricity demand forecast, especially the electricity consumption forecast, is crucial for planning and operating a power system. Due to various unstable factors, it is challenging to forecast electricity consumption. Therefore, it is necessary to establish new models for accurate forecasts. This study proposes a hybrid model, which includes data selection, an abnormality analysis, a feasibility test, and an optimized grey model to forecast electricity consumption. First, the original electricity consumption data are selected to construct different schemes (Scheme 1: short-term selection and Scheme 2: long-term selection; next, the iterative algorithm (IA and cuckoo search algorithm (CS are employed to select the best parameter of GM(1,1. The forecasted day is then divided into several smooth parts because the grey model is highly accurate in the smooth rise and drop phases; thus, the best scheme for each part is determined using the grey correlation coefficient. Finally, the experimental results indicate that the GM(1,1 optimized using CS has the highest forecasting accuracy compared with the GM(1,1 and the GM(1,1 optimized using the IA and the autoregressive integrated moving average (ARIMA model.

  16. Design of online monitoring and forecasting system for electrical equipment temperature of prefabricated substation based on WSN

    Science.gov (United States)

    Qi, Weiran; Miao, Hongxia; Miao, Xuejiao; Xiao, Xuanxuan; Yan, Kuo

    2016-10-01

    In order to ensure the safe and stable operation of the prefabricated substations, temperature sensing subsystem, temperature remote monitoring and management subsystem, forecast subsystem are designed in the paper. Wireless temperature sensing subsystem which consists of temperature sensor and MCU sends the electrical equipment temperature to the remote monitoring center by wireless sensor network. Remote monitoring center can realize the remote monitoring and prediction by monitoring and management subsystem and forecast subsystem. Real-time monitoring of power equipment temperature, history inquiry database, user management, password settings, etc., were achieved by monitoring and management subsystem. In temperature forecast subsystem, firstly, the chaos of the temperature data was verified and phase space is reconstructed. Then Support Vector Machine - Particle Swarm Optimization (SVM-PSO) was used to predict the temperature of the power equipment in prefabricated substations. The simulation results found that compared with the traditional methods SVM-PSO has higher prediction accuracy.

  17. Mortality forecast from gastroduodenal ulcer disease for different gender and age population groups in Ukraine

    Directory of Open Access Journals (Sweden)

    Duzhiy I.D.

    2016-03-01

    Full Text Available Until 2030 the ulcer mortality will have a growing trend as estimated by the World Health Organization. Detection of countries and population groups with high risks for the ulcer mortality is possible using forecast method. The authors made a forecast of mortality rate from complicated ulcer disease in males and females and their age groups (15-24, 25-34, 35-54, 55-74, over 75, 15 - over 75 in our country. The study included data of the World Health Organization Database from 1991 to 2012. The work analyzed absolute all-Ukrainian numbers of persons of both genders died from the ulcer causes (К25-К27 coded by the 10th International Diseases Classification. The relative mortality per 100 000 of alive persons of the same age was calculated de novo. The analysis of distribution laws and their estimation presents a trend of growth of the relative mortality. A remarkable increase of deaths from the ulcer disease is observed in males and females of the age after 55 years old. After the age of 75 years this trend is more expressed.

  18. A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

    Directory of Open Access Journals (Sweden)

    Zhilong Wang

    2014-01-01

    Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

  19. The use of a dense urban meteorological network to enable long term electricity consumption forecasting

    Science.gov (United States)

    Antunes de Azevedo, J.

    2015-12-01

    High air temperatures have an impact on energy consumption, since the demand for cooling fans and air conditioning increases. With current climate projections indicating a general increase in air temperatures, as well as more frequent and intense heat waves, cooling energy demand will increase with time and should therefore be considered by industry and policy makers. Cooling degree days (CDD) are a standard approach used by energy industry to estimate cooling demand. The methodology compares ambient temperatures with a base value for air temperature considered representative of the city being analysed. However, due to the Urban Heat Island effect, temperature and energy consumption will vary considerably across a city. Hence, for CDD to be estimated across an urban area, air temperature data from dense urban networks are required. This study analysed air temperature data available from a dense urban meteorological network to estimate CDD and cooling needs across Birmingham-UK for summer 2013. From the results, it was possible to identify the potential role and limitations of urban meteorological networks in forecasting electricity demand within a city for future climate scenarios.

  20. Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

    Science.gov (United States)

    Dreher, Joseph G.

    2009-01-01

    For expedience in delivering dispersion guidance in the diversity of operational situations, National Weather Service Melbourne (MLB) and Spaceflight Meteorology Group (SMG) are becoming increasingly reliant on the PC-based version of the HYSPLIT model run through a graphical user interface (GUI). While the GUI offers unique advantages when compared to traditional methods, it is difficult for forecasters to run and manage in an operational environment. To alleviate the difficulty in providing scheduled real-time trajectory and concentration guidance, the Applied Meteorology Unit (AMU) configured a Linux version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (HYSPLIT) model that ingests the National Centers for Environmental Prediction (NCEP) guidance, such as the North American Mesoscale (NAM) and the Rapid Update Cycle (RUC) models. The AMU configured the HYSPLIT system to automatically download the NCEP model products, convert the meteorological grids into HYSPLIT binary format, run the model from several pre-selected latitude/longitude sites, and post-process the data to create output graphics. In addition, the AMU configured several software programs to convert local Weather Research and Forecast (WRF) model output into HYSPLIT format.

  1. Electricity Price Forecast Based on FOALSSVM Model in Electricity Market Environment%电力市场环境下基于FOALSSVM的电价预测

    Institute of Scientific and Technical Information of China (English)

    谭忠富; 何楠; 周凤翱

    2012-01-01

    提出了一种基于果蝇优化算法(FOA)和最小二乘支持向量机(LSSVM)模型的日均电价混合预测模型。将日均电价的历史数据和负荷数据作为输入变量,利用FOA优化选择用于电价预测的LSSVM模型最优参数值,进而对日均电价进行预测。以澳大利亚NSW电力市场的实际数据为例对该模型进行了仿真测试,其结果表明:与自适应LSSVM、模拟退火LSSVM和ARIMA-GARCH模型相比,本文提出的预测模型的预测性能最好,其收敛速度快,预测精度高。%A hybrid daily average electricity price forecasting model based on fruit flies optimization algorithm(FOA) and least squares support vector machine(LSSVM) model is proposed.The historical data of daily average electricity price and load data are taken as the input variables;the optimal parameter values of LSSVM model are selected by use of FOA;the daily average electricity price is thus forecast.The simulation test based on the actual data of Australia NSW electricity market was performed.The result shows that this proposed model has achieved better forecasting performance due to its faster convergence speed and higher forecasting accuracy compared with self-adaptive LSSVM,simulated annealing LSSVM,and ARIMA-GARCH model.

  2. Electricity Consumption and Economic Growth: Analysis and Forecasts using VAR/VEC Approach for Greece with Capital Formation

    Directory of Open Access Journals (Sweden)

    Andreas Georgantopoulos

    2012-01-01

    Full Text Available This paper tests for the existence and direction of causality between electricity consumption and real gross domestic product for Greece. The study examines a trivariate system with capital formation for the period 1980-2010. Robust empirical results indicate that all variables are integrated of order one and cointegration analysis reports that cointegrating relationship exists between the variables. VAR/VEC approach suggests that all variables return to the long-run equilibrium whenever there is a deviation from the cointegrating relationship and that unidirectional causal links exists running from capital formation and electricity consumption to RGDP in the short-run implying that the economy of Greece is strongly energy dependent. Forecasts for the period 2011-2020 indicate increasing consumption of electricity and positive growth rates from 2013. Policy makers will need to liberalise the electricity sector and to turn the economy towards renewable and natural gas sources in order to reduce imports of oil and coal dependency.

  3. 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

  4. CHINALCO Repurchased Equity of Ningxia Electric Power Group

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

    <正>On the transformation road to promoting coal-power-aluminum integration, CHINALCO moved another step forward. Following the acquisition of 35.3% equity of Ningxia Electric Power Group in August, CHINALCO again announced to buy 23.66% equity of Ningxia

  5. Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

    Directory of Open Access Journals (Sweden)

    Yan Hong Chen

    2016-01-01

    Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.

  6. Forecasting the State of Health of Electric Vehicle Batteries to Evaluate the Viability of Car Sharing Practices

    Directory of Open Access Journals (Sweden)

    Ivana Semanjski

    2016-12-01

    Full Text Available Car-sharing practices are introducing electric vehicles (EVs into their fleet. However, the literature suggests that at this point shared EV systems are failing to reach satisfactory commercial viability. A potential reason for this is the effect of higher vehicle usage, which is characteristic of car sharing, and the implications on the battery’s state of health (SoH. In this paper, we forecast the SoH of two identical EVs being used in different car-sharing practices. For this purpose, we use real life transaction data from charging stations and different EV sensors. The results indicate that insight into users’ driving and charging behavior can provide a valuable point of reference for car-sharing system designers. In particular, the forecasting results show that the moment when an EV battery reaches its theoretical end of life can differ in as much as a quarter of the time when vehicles are shared under different conditions.

  7. 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.  

  8. Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

    Directory of Open Access Journals (Sweden)

    Antonio Bello

    2016-03-01

    Full Text Available One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.

  9. Research and Application of Fire Forecasting Model for Electric Transmission Lines Incorporating Meteorological Data and Human Activities

    Directory of Open Access Journals (Sweden)

    Jiazheng Lu

    2016-01-01

    Full Text Available Recently, there is a rise in frequency of fires which pose a serious threat to the safety operation of electric transmission lines. Several ultrahigh voltage (UHV electric transmission lines, including Fufeng line, Jinsu line, Longzheng line, and Changnan line, showed many times tripping or bipolar latching caused by fire disasters. Fire disasters have tended to be the biggest threat to the safety operation of electric transmission lines and even can cause power grid collapse in some severe situations. Researchers have made much research on fires forecasting. However, these studies are mainly concentrated on predicting fires based on measured or forecasting meteorological data and do not take into account the effect of human activities. In fact, fire disasters have a very close relationship with human activities. In our research, a fire prediction model is proposed incorporating meteorological data as well as human activities. And this model is applied in Hunan province and Anhui province, which seriously suffer from fire disasters. The results show that the model has good prediction precision and can be a powerful tool for practical application.

  10. 77 FR 70484 - Preoperational Testing of Onsite Electric Power Systems To Verify Proper Load Group Assignments...

    Science.gov (United States)

    2012-11-26

    ... COMMISSION Preoperational Testing of Onsite Electric Power Systems To Verify Proper Load Group Assignments... Power Systems to Verify Proper Load Group Assignments, Electrical Separation, and Redundancy.'' DG-1294... encompass preoperational testing of electrical power systems used to meet current Station...

  11. Actions to promote energy efficient electric motors. Motors study group

    Energy Technology Data Exchange (ETDEWEB)

    Almeida, A.T. de [Coimbra Univ. (PT). Inst. of Systems and Robotics (ISR)

    1996-10-01

    Motor electricity consumption is influenced by many factors including: motor efficiency, motor speed controls, power supply quality, harmonics, systems oversizing, distribution network, mechanical transmission system, maintenance practices, load management and cycling, and the efficiency of the end-use device (e.g. fan, pump, etc.). Due to their importance, an overview of these factors is presented in this report. This study also describes the electricity use in the industrial and tertiary sectors and the electricity consumption associated with the different types of electric motors systems in the Member States of the European Union, as well as estimated future evolution until 2010. The studies for individual countries were carried out by the different partners of the motors study group at a previous stage. The study has found that there is a lack of accurate information about the motor electricity consumption, installed motor capacity and the motor market in almost all the European Union countries and only some general statistical sources are available. There is little field data, which is mainly available in Denmark, France, Italy and the Netherlands. Due to this lack of primary information, some common assumptions were made, based on the experience of the members of the study group. This lack of end-use characterisation data shows the need for improvement from the point of view of current knowledge. It is therefore recommended that further research is undertaken to arrive at more accurate figures. These could be the basis for a better understanding for motor use in practice and - as a consequence - for a more precise appraisal of potentials and barriers to energy efficiency. (orig.)

  12. 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.

  13. 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.  

  14. MODERN SITUATION AND FORECASTS OF ELECTRICAL POWER ENGINEERING DEVELOPMENT IN THE REPUBLIC OF BELARUS AND RUSSIAN FEDERATION

    Directory of Open Access Journals (Sweden)

    V. V. Kravchenko

    2008-01-01

    Full Text Available On the basis of systematic analysis of scientific, statistical and economic data the paper compares modern situation and forecasts of electrical power engineering development in the Republic of Belarus and Russian Federation. The paper carries out an analysis of integrated structure of fuel balances of both countries till 2015. The paper notes the fact that thermal power stations (TPS will remain a main generating source till 2020 and gas will continue to be the main type of fuel in the structure of fuel balances. The paper investigates development of technological structures in the electrical power engineering. It has been revealed that one of the main factors that hinders development of the Belarussian power system is the absence of the required financial  mechanisms for obtaining additional investment possibilities. In connection with this fact a special attention should be given to the problems that are directed on improvement of tariff policy and mechanisms of tariff formation.

  15. A Self-Adapting Approach for Forecast-Less Scheduling of Electrical Energy Storage Systems in a Liberalized Energy Market

    Directory of Open Access Journals (Sweden)

    Ninh Nguyen Quang

    2013-11-01

    Full Text Available In this paper, an original scheduling approach for optimal dispatch of electrical Energy Storage Systems (ESS in modern distribution networks is proposed. The control system is based on fuzzy rules and does not use forecasts since it repairs the past history according to the real time data on the electrical energy cost, renewable energy production and load. When the system detects a worsening of performances, the fuzzy logic rule-based control system self-adapts its membership functions using an economic indicator. The common use, in the relevant literature, of forecasted values in such systems can lead to large errors and economic losses. Moreover the speed of calculation guaranteed by the fuzzy control system allows the execution of new calculations even with high frequency. After the Introduction section, where the state of the art on the topic is outlined, the problem formulation is presented and an interesting application of the considered approach to the control on a medium size battery with real world data is proposed.

  16. A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

    OpenAIRE

    Francisco Martínez-Álvarez; Alicia Troncoso; Gualberto Asencio-Cortés; Riquelme, José C

    2015-01-01

    Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of ...

  17. Energy forecast. Final report; Energiudsigten. Slutrapport

    Energy Technology Data Exchange (ETDEWEB)

    2010-04-15

    A number of instruments, i.e. Internet, media campaigns, boxes displaying electricity prices (SEE1) and spot contract has been tested for households to shift their electricity consumption to times when prices are low. Of the implemented media campaigns, only the daily viewing of Energy forecast on TV had an impact. Consumers gained greater knowledge of electricity prices and electricity consumption loads, but only showed little interest in shifting electricity consumption. However, a measurable effect appeared at night with the group that had both concluded a spot contract and received an SEE1. These factors increase the awareness of the price of electricity and the possibility of shifting electricity consumption. (Energy 10)

  18. Market data analysis and short-term price forecasting in the Iran electricity market with pay-as-bid payment mechanism

    Energy Technology Data Exchange (ETDEWEB)

    Bigdeli, N.; Afshar, K. [EE Department, IKIU, Qazvin (Iran); Amjady, N. [EE Department, Semnan University, Semnan (Iran)

    2009-06-15

    Market data analysis and short-term price forecasting in Iran electricity market as a market with pay-as-bid payment mechanism has been considered in this paper. The data analysis procedure includes both correlation and predictability analysis of the most important load and price indices. The employed data are the experimental time series from Iran electricity market in its real size and is long enough to make it possible to take properties such as non-stationarity of market into account. For predictability analysis, the bifurcation diagrams and recurrence plots of the data have been investigated. The results of these analyses indicate existence of deterministic chaos in addition to non-stationarity property of the system which implies short-term predictability. In the next step, two artificial neural networks have been developed for forecasting the two price indices in Iran's electricity market. The models' input sets are selected regarding four aspects: the correlation properties of the available data, the critiques of Iran's electricity market, a proper convergence rate in case of sudden variations in the market price behavior, and the omission of cumulative forecasting errors. The simulation results based on experimental data from Iran electricity market are representative of good performance of the developed neural networks in coping with and forecasting of the market behavior, even in the case of severe volatility in the market price indices. (author)

  19. Price forecast and pricing models on the options exchange for electric power. Information material - documentation; Preisprognose und Preismodelle am Stromterminmarkt. Materialienband

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2006-07-01

    The development of the future prices for electric power calls for a continuous analysis of the market. The quantities to be sold are purchased directly on the wholesale-trading market or via models indicating the development of the wholesale trading prices. A professional and risk-adjusted control of the portfolio is the central module in order to get the right position in the increasing competition. Within the options trading, forecasting the electric power prices is of central importance.

  20. Developing long-term scenario forecasts to support electricity generation investment decisions

    CSIR Research Space (South Africa)

    Koen, Renée

    2014-09-01

    Full Text Available Many decisions regarding capital investment in electricity generation technologies need to be made well in advance, usually when there is still a large amount of uncertainty regarding the favourability of future conditions. There may be uncertainty...

  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. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques.

    Science.gov (United States)

    Chen, Shyi-Ming; Manalu, Gandhi Maruli Tua; Pan, Jeng-Shyang; Liu, Hsiang-Chuan

    2013-06-01

    In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.

  3. Multi nodal load forecasting in electric power systems using a radial basis neural network; Previsao de carga multinodal em sistemas eletricos de potencia usando uma rede neural de base radial

    Energy Technology Data Exchange (ETDEWEB)

    Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br

    2009-07-01

    This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)

  4. 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.

  5. Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD—Based Multiscale Methodology

    Directory of Open Access Journals (Sweden)

    Kaijian He

    2016-11-01

    Full Text Available The electricity market has experienced an increasing level of deregulation and reform over the years. There is an increasing level of electricity price fluctuation, uncertainty, and risk exposure in the marketplace. Traditional risk measurement models based on the homogeneous and efficient market assumption no longer suffice, facing the increasing level of accuracy and reliability requirements. In this paper, we propose a new Empirical Mode Decomposition (EMD-based Value at Risk (VaR model to estimate the downside risk measure in the electricity market. The proposed model investigates and models the inherent multiscale market risk structure. The EMD model is introduced to decompose the electricity time series into several Intrinsic Mode Functions (IMF with distinct multiscale characteristics. The Exponential Weighted Moving Average (EWMA model is used to model the individual risk factors across different scales. Experimental results using different models in the Australian electricity markets show that EMD-EWMA models based on Student’s t distribution achieves the best performance, and outperforms the benchmark EWMA model significantly in terms of model reliability and predictive accuracy.

  6. Long term forecasting of hourly electricity consumption in local areas in Denmark

    DEFF Research Database (Denmark)

    Møller Andersen, Frits; Larsen, Helge V.; Gaardestrup, R.B.

    2013-01-01

    Long term projections of hourly electricity consumption in local areas are important for planning of the transmission grid. In Denmark, at present the method used for grid planning is based on statistical analysis of the hour of maximum load and for each local area the maximum load is projected...... to change proportional to changes in the aggregated national electricity consumption. That is, specific local conditions are not considered. Yet, from measurements of local consumption we know that:. •consumption profiles differ between local areas,•consumption by categories of customers contribute....... The model describes the entire profile of hourly consumption and is a first step towards differentiated local predictions of electricity consumption.The model is based on metering of aggregated hourly consumption at transformer stations covering selected local areas and on national statistics of hourly...

  7. 76 FR 53888 - Application to Export Electric Energy; Morgan Stanley Capital Group Inc.

    Science.gov (United States)

    2011-08-30

    ... Application to Export Electric Energy; Morgan Stanley Capital Group Inc. AGENCY: Office of Electricity... Inc. (MSCG) has applied to renew its authority to transmit electric energy from the United States to...-year term. The electric energy that MSCG proposes to export to Mexico would be surplus energy...

  8. 75 FR 22579 - Application To Export Electric Energy; Morgan Stanley Capital Group Inc.

    Science.gov (United States)

    2010-04-29

    ... Application To Export Electric Energy; Morgan Stanley Capital Group Inc. AGENCY: Office of Electricity... Inc. (MSCG) has applied to renew its authority to transmit electric energy from the United States to...-185 authorizing MSGC to transmit electric energy from the United States to Canada as a power...

  9. 77 FR 11515 - Application To Export Electric Energy; Pilot Power Group, Inc.

    Science.gov (United States)

    2012-02-27

    ... Application To Export Electric Energy; Pilot Power Group, Inc. AGENCY: Office of Electricity Delivery and...) has applied for authority to transmit electric energy from the United States to Mexico pursuant to... Power for authority to transmit electric energy from the United States to Mexico for five years as...

  10. Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models

    Energy Technology Data Exchange (ETDEWEB)

    Neto, Joao C. do L, E-mail: jcaldas@ufam.edu.br [Group of Optimization and Fuzzy Systems, Federal University of Amazonas, General Rodrigo Octavio Jordao Ramos Avenue, 3000, Academic Campus, 69077-000 Manaus, Amazonas (Brazil); Costa Junior, Carlos T. da [Postgraduate Program in Electrical Engineering, Institute of Technology, Federal University of Para, Augusto Correa Street, 1, Guama, 66075-900 Belem, Para (Brazil); Bitar, Sandro D.B. [Group of Optimization and Fuzzy Systems, Federal University of Amazonas, General Rodrigo Octavio Jordao Ramos Avenue, 3000, Academic Campus, 69077-000 Manaus, Amazonas (Brazil); Junior, Walter B. [Postgraduate Program in Electrical Engineering, Institute of Technology, Federal University of Para, Augusto Correa Street, 1, Guama, 66075-900 Belem, Para (Brazil)

    2011-09-15

    Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the {alpha}-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs. - Highlights: > A decision support system has been developed using SARIMA with fuzzy systems and optimizations tools. > It assists the decision-making process for planning the expansion in isolated thermoelectric systems. > The {alpha}-cut concept facilitated the evaluation of risks for the cost of electricity production. > Provides decisions using various forecasted interval for this cost with different membership values.

  11. Oracle Efficient Estimation and Forecasting with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Callot, Laurent

    We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated consistently...... at root T rate, that the truly zero parameters are classiffied as such asymptotically and that the non-zero parameters are estimated as efficiently as if only the relevant variables had been included in the model from the outset. The group adaptive Lasso differs from the adaptive Lasso by dividing...... the covariates into groups whose members are all relevant or all irrelevant. Both estimators have the property that they perform variable selection and estimation in one step. We evaluate the forecasting accuracy of these estimators for a large set of macroeconomic variables. The Lasso is found to be the most...

  12. Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

    Science.gov (United States)

    2013-12-01

    geothermal energy, the DoD’s wind power production is still insignificant, despite the fact that wind power is one of the most abundant and promising...million American homes, or the same amount of electricity as 10 nuclear power plants [16]. This 60 GW wind power capacity reduces carbon dioxide (CO2...thermal power plants [16]. 8 Figure 4. Global cumulative installed wind capacity 1996-2012 (from [18]) Despite its abundance, wind energy is

  13. 基于回声状态网络的电力市场电价预测%Echo-state-network based electricity price forecasting in electric power market

    Institute of Scientific and Technical Information of China (English)

    任远

    2016-01-01

    Traditional neural network based electricity price forecasting algorithm fails to meet current demands by future electric power market, with low accuracy and long computation time when the electric power price changes greatly. Using the method based on Echo-State-Network (ESN), an electricity power price short-term forecasting approach is proposed. Firstly, the principle of ESN is introduced and discussed. On this basis, the electricity power price short-term forecasting approach is proposed, including parameter selection, sampling data pre-processing and ESN training and forecast process. Then, the short-term electricity price forecasting is performed by ESN and BP neural network. The simulation results show that using ESN the short-term electricity price can be forecasted more quickly and steadily.%传统的神经网络算法在电价变化剧烈的情况下,精度较低并且所耗费的时间较长,难以满足电力市场发展的需求。为解决该问题,提出了一种基于回声状态网络(ESN)的短期电价预测方法。所提方法介绍了基于回声状态网络的预测原理,提出了电力市场短期电价的预测机制,包括参数选取、采样数据预处理和 ESN 训练及预测过程;并分别采用回声状态网络和反向传播算法(BP)神经网络进行短期电价预测。经过仿真验证,所提出的基于回声状态网络的电价预测具有较好的准确率和可行性。

  14. 77 FR 47043 - Work Group on Measuring Systems for Electric Vehicle Fueling

    Science.gov (United States)

    2012-08-07

    ... National Institute of Standards and Technology Work Group on Measuring Systems for Electric Vehicle Fueling... devices and systems used to assess charges to consumers for electric vehicle fuel. There is no cost for... residential and business locations and those used to measure and sell electricity dispensed as a vehicle fuel...

  15. Using electric fields for pulse compression and group velocity control

    CERN Document Server

    Li, Qian; Thuresson, Axel; Rippe, Lars; Kröll, Stefan

    2016-01-01

    In this article, we experimentally demonstrate a new way of controlling the group velocity of an optical pulse by using a combination of spectral hole burning, slow light effect and linear Stark effect in a rare-earth-ion-doped crystal. The group velocity can be changed continuously by a factor of 20 without significant pulse distortion or absorption of the pulse energy. With a similar technique, an optical pulse can also be compressed in time. Theoretical simulations were developed to simulate the group velocity control and the pulse compression processes. The group velocity as well as the pulse reshaping are solely controlled by external voltages which makes it promising in quantum information and quantum communication processes. It is also proposed that the group velocity can be changed even more in an Er doped crystal while at the same time having a transmission band matching the telecommunication wavelength.

  16. 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.

  17. Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts

    OpenAIRE

    Bidong Liu; Jakub Nowotarski; Tao Hong; Rafal Weron

    2015-01-01

    Majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a prac...

  18. 短期电力负荷预测研究%Study on short term electric load forecasting

    Institute of Scientific and Technical Information of China (English)

    许祎; 王世芳

    2015-01-01

    Power system load forecasting through the historical data analysis,forecast future demand. In this paper,we use wavelet clustering to load data.Then we use the classical genetic algorithm,Elman neural network,wavelet neural network and combined intelligent algorithm to build the forecasting model.By comparing the simulation results of several short-term power load forecasting models,the hybrid intelligent algorithm can greatly enhance the accuracy and reliability of the load forecasting results,and has good application prospect.%电力系统负荷预测通过对历史数据分析,预测未来需求。本文先用小波聚类对数据进行负荷分类,再分别用经典的遗传算法、Elman神经网络算法、小波-神经网络算法和组合智能算法建立预测模型。通过比较以上几种短期电力负荷预测模型的仿真结果,验证了混合智能算法可以大大增强负荷预测结果的准确性和可靠性,具有良好的应用前景。

  19. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships.

    Science.gov (United States)

    Chen, Shyi-Ming; Chen, Shen-Wen

    2015-03-01

    In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the "down-trend," the probability of the "equal-trend" and the probability of the "up-trend" of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the down-trend, the equal-trend, and the up-trend of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.

  20. 基于组间进位预测的快速进位加法器%Rapid Carry Adder Based on Carry Forecast Between Groups

    Institute of Scientific and Technical Information of China (English)

    丁宜栋; 刘昌明; 方湘艳

    2011-01-01

    为加快密码系统中大数加法的运算速度,提出并实现一种基于组间进位预测的快速进位加法器.将参与加法运算的大数进行分组,每个分组采用改进的超前进位技术以减少组内进位延时,组间通过进位预测完成不同进位状态下的加法运算,通过每个组产生的进位状态判断最终结果.性能分析表明,该进位加法器实现1024位大数加法运算的速度较快.%This paper presents and realizes a rapid carry adder based on carry forecast between groups to improve the speed of the large numbers adder in some cryptography systems. The large numbers is divided into many groups, the delay of carry-chain is reduced by carry lookahead in group. The group addition operation of different carry state is finished by carry forecast between groups. The addition sun of different carry forecast state is selected as the final result based on the carry state. Performance analysis shows that the computing speed of the carry adder is faster when realizing the 1 024 bit large numbers add operation.

  1. 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.

  2. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-09-01

    Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

  3. Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model

    OpenAIRE

    Roldán Blay, Carlos; Escrivá-Escrivá, Guillermo; Álvarez Bel, Carlos María; Roldán Porta, Carlos; Rodriguez-Garcia, Javier

    2013-01-01

    This paper presents the upgrading of a method for predicting short-term building energy consumption that was previously developed by the authors (EUs method). The upgrade uses a time temperature curve (TTC) forecast model. The EUs method involves the use of artificial neural networks (ANNs) for predicting each independent process end-uses (EUs). End-uses consume energy with a specific behaviour in function of certain external variables. The EUs method obtains the total consumption by the ad...

  4. Analysis of thermal characteristics of electrical wiring for load groups in cattle barns.

    Science.gov (United States)

    Kim, Doo Hyun; Yoo, Sang-Ok; Kim, Sung Chul; Hwang, Dong Kyu

    2015-01-01

    The purpose of the current study is to analyze the thermal characteristics of electrical wirings depending on the number of operating load by connecting four types of electrical wirings that are selected by surveying the conditions for the electric fans, automatic waterers and halogen warm lamps that were installed in cattle barns in different years. The conditions of 64 cattle barns were surveyed and an experimental test was conducted at a cattle barn. The condition-survey covered inappropriate design, construction and misuse of electrical facility, including electrical wiring mostly used, and the mode of load current was evaluated. The survey showed that the mode of load current increased as the installation year of the fans, waterers and halogen lamps became older. Accordingly, the cattle barn manager needed to increase the capacity of the circuit breaker, which promoted the degradation of insulation of the electrical wires' sheath and increased possibility for electrical fires in the long-run. The test showed that the saturation temperature of the wire insulated sheath increased depending on the installation year of the load groups, in case of VCTFK and VFF electric wires, therefore, requiring their careful usage in the cattle barns.

  5. Load forecasting of supermarket refrigeration

    DEFF Research Database (Denmark)

    Rasmussen, Lisa Buth; Bacher, Peder; Madsen, Henrik

    2016-01-01

    This paper presents a novel study of models for forecasting the electrical load for supermarket refrigeration. The data used for building the models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village...... in Denmark. Every hour the hourly electrical load for refrigeration is forecasted for the following 42 h. The forecast models are adaptive linear time series models. The model has two regimes; one for opening hours and one for closing hours, this is modeled by a regime switching model and two different...

  6. 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.

  7. 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.

  8. Report of the Task Group on Electrical Safety of Department of Energy facilities

    Energy Technology Data Exchange (ETDEWEB)

    None

    1993-01-01

    The Task Group on Electrical Safety at DOE Facilities (Task Group), which was formally established on October 27, 1992. The Task Group reviewed the electrical safety-related occurrence history of, and conducted field visits to, seven DOE sites chosen to represent a cross section of the Department`s electrical safety activities. The purpose of the field visits was to review, firsthand, electrical safety programs and practices and to gain greater insight to the root causes and corrective actions taken for recently reported incidents. The electrical safety environment of the DOE complex is extremely varied, ranging from common office and industrial electrical systems to large high-voltage power distribution systems (commercial transmission line systems). It includes high-voltage/high-power systems associated with research programs such as linear accelerators and experimental fusion confinement systems. Age, condition, and magnitude of the facilities also varies, with facilities dating from the Manhattan Project, during World War II, to the most modem complexes. The complex is populated by Federal (DOE and other agencies) and contractor employees engaged in a wide variety of occupations and activities in office, research and development, and industrial settings. The sites visited included all of these variations and are considered by the Task Group to offer a valid representation of the Department`s electrical safety issues. The sites visited were Oak Ridge National Laboratory (ORNL), Stanford Linear Accelerator Center (SLAC), Idaho National Engineering Laboratory (INEL), Nevada Test Site (NTS), Savannah River Site (SRS), Hanford Reservation (Hanford), and the Uranium Mill Tailings Remedial Action Project (UMTRA) located at Grand Junction, Colorado.

  9. Electricity Price Forecasting Using Wavelet Neural Networks Optimized by GA%小波神经网络预测电价的新改进

    Institute of Scientific and Technical Information of China (English)

    涂启玉; 张茂林

    2011-01-01

    预测市场边际电价对于电力市场的参与者有十分重要的意义.该文首先分析了BP神经网络在电价预测方面的优劣势,然后基于小波分析,即用母小波取代Sigmoid函数建立了小波神经网络的电价预测模型,并用遗传算法优化神经网络的拓扑结构和各权重系数,从而避免BP神经网络的预测电价陷入局部极小值.实际计算表明,改进后的预测模型有效地提高了预测精度.%Forecasting the future electricity price is very important for every participators in the power market.This paper analyses the advantages and disadvantages of BP neural networks, then provides a wavelet-improved neural network model base on wavelet analysis and the topology structure and weight coefficient optimized by GA. Application to the real system shows that this model can improve the forecasting precision and avoid the limitation of the BP neural networks.

  10. Comparison of the Short-Term Forecasting Accuracy on Battery Electric Vehicle between Modified Bass and Lotka-Volterra Model: A Case Study of China

    Directory of Open Access Journals (Sweden)

    Shunxi Li

    2017-01-01

    Full Text Available The potential demand of battery electric vehicle (BEV is the base of the decision-making to the government policy formulation, enterprise manufacture capacity expansion, and charging infrastructure construction. How to predict the future amount of BEV accurately is very important to the development of BEV both in practice and in theory. The present paper tries to compare the short-term accuracy of a proposed modified Bass model and Lotka-Volterra (LV model, by taking China’s BEV development as the case study. Using the statistics data of China’s BEV amount of 21 months from Jan 2015 to Sep 2016, we compare the simulation accuracy based on the value of mean absolute percentage error (MAPE and discuss the forecasting capacity of the two models according to China’s government expectation. According to the MAPE value, the two models have good prediction accuracy, but the Bass model is more accurate than LV model. Bass model has only one dimension and focuses on the diffusion trend, while LV model has two dimensions and mainly describes the relationship and competing process between the two populations. In future research, the forecasting advantages of Bass model and LV model should be combined to get more accurate predicting effect.

  11. Role of design complexity in forecasting reliability and availability for electric-power-generating units. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Berkey, D.M.; Balaban, H.S.

    1982-10-01

    This report examines the relationship between design complexity and reliability and availability performance for fossil-fueled electric-power-generating units. Multivariate regression analysis was applied to design complexity and reliability and availability performance data gathered from a representative sample of electric-power-generating units. Twelve predictive relationships or equations were developed as a result of employing this statistical procedure. Each equation was verified and assessed. Guidelines for applying the predictive relationships, including confidence limits, were also developed and are presented in this report. A major result of this examination is a quantitative predictive tool that should be useful to the electric-power industry.

  12. A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

    Directory of Open Access Journals (Sweden)

    Lianhui Li

    2015-12-01

    Full Text Available Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.

  13. Electric vehicle market penetration in Switzerland by 2020 - We cannot forecast the future but we can prepare for it

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2009-07-15

    The road transportation sector in Switzerland accounts for 44% of the whole greenhouse gas emissions of the country (around 52 million tons of CO{sub 2} equivalent, of which around 44 million tons of CO{sub 2}). The share of private cars is 72% of the road transportation emissions. The efficiency of electric vehicles is near 40% (useful energy/primary energy) in comparison to that of fossil fuel vehicles (15-20%). The European Union (EU) market average of CO{sub 2} emissions from passenger cars was about 160 g CO{sub 2}/km in 2005 and the average EU mix of electricity production had specific emissions of 410 g CO{sub 2}/kWh in the same year. In comparison the Swiss production mix was 34 CO{sub 2}/kWh in 2005, but the relevant Swiss consumption mix was 112 g CO{sub 2}/kWh, due to imports of electricity (with around 21% of the demand covered by imports). Hence a typical electric car will produce CO{sub 2} emissions of around 80 g CO{sub 2}/km in Europe, what is already twice better (in Switzerland: 23 g CO{sub 2}/km with the present consumption mix). By 2030 it is assumed that the EU electricity production mix will diminish to 130 g CO{sub 2}/kWh, and in Switzerland the consumption mix would be around 55 g CO{sub 2}/kWh (still calculated with 21% imports and with the same Swiss production mix), resulting in emissions from electric car in Europe of less than 30 g CO{sub 2}/km, and in Switzerland less than 13 g CO{sub 2}/km (all calculations made with a specific electric demand of 18-20 kWh/100 km). In summary, electrical vehicles retain a tremendous comparative advantage with respect to internal combustion engine vehicles. If 15% of the Swiss cars (i.e. 720,000 units) would be replaced by electrical vehicles, yearly CO{sub 2} emissions would decrease by about 1.2 million tons. This figure must be compared with the international commitment of Switzerland concerning its reduction of the global greenhouse gas emissions by 20%, i.e. 10.5 million tons of CO{sub 2

  14. Econometrics 101: forecasting demystified

    Energy Technology Data Exchange (ETDEWEB)

    Crow, R.T.

    1980-05-01

    Forecasting by econometric modeling is described in a commonsense way which omits much of the technical jargon. A trend of continuous growth is no longer an adequate forecasting tool. Today's forecasters must consider rapid changes in price, policies, regulations, capital availability, and the cost of being wrong. A forecasting model is designed by identifying future influences on electricity purchases and quantifying their relationships to each other. A record is produced which can be evaluated and used to make corrections in the models. Residential consumption is used to illustrate how this works and to demonstrate how power consumption is also related to the purchase and use of equipment. While models can quantify behavioral relationships, they cannot account for the impacts of non-price factors because of limited data. (DCK)

  15. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    indicated that forecasting experience has little relationship to forecasting performance. In the latter three studies, neophyte forecasters became... Europe . Within a few months after a new commander was assigned, this unit’s performance rose to first place in the theater and remained there

  16. 安徽省废弃电子电器产品回收网点建设需求预测分析%Anhui Waste Electrical and Electronic Product Recycling Network Construction Demand Forecasting Analysis

    Institute of Scientific and Technical Information of China (English)

    于蕾

    2014-01-01

    Based on the population projections,Anhui,Anhui amount of waste products recycling electrical and electronic products and electrical and electronic waste produced in Anhui Province and processing capabilities forecast demand forecast study electrical and electronic products recycling outlets Anhui abandoned building demand forecasting,order Anhui Province to establish waste electrical and electronic recycling centers to reference.%文中通过对安徽省人口预测、安徽省废弃电子电器产品的产品回收量和安徽省废弃电子电器产生量预测及处理能力需求预测,探讨了安徽省废弃电子电器产品回收网点建设需求预测,以期对安徽省废旧电子电器回收处理中心的建立起到参考作用。

  17. [The pneumoperitoneum course forecasting and surgery tactic in the group of patients with acute and chronic cholecystitis and concomitant pathology of cardiovascular system].

    Science.gov (United States)

    Korotkyĭ, V M; Soliaryk, S O; Tsyganok, A M; Sysak, O M

    2012-01-01

    The share of elderly and senile patients with acute cholecystitis concomitant cardiovascular pathology whom the laparoscopic cholecystectomy has been provided is increased. The heightened intraabdominal pressure has negative influence at the cardiovascular system, so the alternative ways for treatment of this group of patients are used in clinic. We propose the pneumoperitoneum model using the pneumatic belt which is fixed at the abdomen in preoperative period in patients with an acute and chronic cholecystitis. This model is useful to forecast cardiovascular disorders during future laparoscopic cholecystectomy. The arterial pressure level, pulse score and ECG are monitored during the test (90 min). Myocardial ischemia appearance seems that the risk of laparoscopic cholecystectomy with pneumoperitoneum is high. The alternative method of surgery in such group of patients (no pneumoperitoneum is applied) is laparoscopic assisted cholecystectomya from miniaccess. This method allows to reducing frequency of intra- and postoperative complications connected with pneumoperitoneum negative influence at the patients with concomitant pathology of cardiovascular system.

  18. Application of semi parametric modelling to times series forecasting: case of the electricity consumption; Modeles semi-parametriques appliques a la prevision des series temporelles. Cas de la consommation d'electricite

    Energy Technology Data Exchange (ETDEWEB)

    Lefieux, V

    2007-10-15

    Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)

  19. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing and Artificial Intelligence Models (ANN, SVM: The Case of Greek Electricity Market

    Directory of Open Access Journals (Sweden)

    George P. Papaioannou

    2016-08-01

    Full Text Available In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC technique and the traditional multiple regression (PC-regression, for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN and Support Vector Machines (SVM. Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

  20. METHODOLOGICAL FOUNDATIONS OF ANALYSIS AND FORECASTING OF GAS CONSUMPTION IN THE SYSTEM OF ENERGY BALANCE OF UKRAINE BY USING THE GROUP METHOD OF DATA HANDLING

    Directory of Open Access Journals (Sweden)

    V. S. Stepashko

    2017-03-01

    Full Text Available This research deals with issues of gas consumption in Ukraine. The dynamics of consumption of gas is analysed and proposed guidelines for the efficient production, consumption and import of gasin Ukraine. Constructed and developed predictive models of gas consumption in Ukraine through the use of modern software and using the group method of data handling, which allowed building adequate predictive models of gas consumption in the system of Ukraine's energy balance. Researched and forecasted scenarios of gas consumption in the Ukraine. The problem of efficient use of energy resources is critical for sustainable economic development against the backdrop of energy saving national economy depends on energy imports, on the one hand, and rising prices for these resources. The basic foundation of the formation energy system of Ukraine is to build forecasting scenarios for different types of energy and different criteria for effective use of energy resources. Solving this problem is not only with ensuring energy security, but also with the level of development of regions of Ukraine and ensuring quality of life. Prediction of gas consumption in Ukraine today is an extremely important issue of strategic importance since conducted through analysis and building predictive models will be possible to develop guidelines for the efficient production and consumption of gas across Ukraine as a whole.

  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. 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)

  3. An integrated model for a forecasting model of the electric power market in the long term; Um modelo integrado de previsao do mercado de energia eletrica a longo prazo

    Energy Technology Data Exchange (ETDEWEB)

    Faria, Sergio Nilo Gomes

    1993-07-01

    A proposal for a forecasting model of the electricity market which, in methodological terms, is based on classic econometric formulations - evaluation of income and price elasticities. The electricity demand for some industrial sectors is dealt with in a desegregated way, in order to capture its dependence on the economic activity of these sectors is presented. The proposal of this thesis differs from the usual methodology as far as evaluating the impacts of the energy demand forecast, conformed to well defined macroeconomics and tariff policy assumptions, on the expansion of the power system as a whole, and, particularly, on the financial situation of the power sector. The motivation for the study was the need for a new methodological tool, broad, but streamlined enough to allow widespread assessments of alternative development scenarios associated to different economic and politic contexts, taking into account the main uncertainties present in the several planning stages. (author)

  4. Technical and Economic Forecast in Selection of Optimum Biomass and Local Fossil Fuel Application Technology for Thermal Electric Energy Generation

    Directory of Open Access Journals (Sweden)

    I. A. Bokun

    2010-01-01

    Full Text Available The paper provides a technical and economic analysis pertaining to selection of optimum biomass and local fossil fuel application technology for thermal electric energy generation while using a matrix of costs and a method of minimum value. Calculation results give grounds to assert that it is expedient to burn in the boiling layer – 69 % and 31 % of wood pellets and wastes, respectively and 54 % of peat and 46 % of slate stones. A steam and gas unit (SGU can fully operate on peat. Taking into account reorientation on decentralized power supply and increase of small power plants up to 3–5 MW the paper specifies variants of the most efficient technologies for burning biomass and local fossil fuels. 

  5. Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zheng Chen

    2017-03-01

    Full Text Available This paper proposes an optimal grouping method for battery packs of electric vehicles (EVs. Based on modeling the vehicle powertrain, analyzing the battery degradation performance and setting up the driving cycle of an EV, a genetic algorithm (GA is applied to optimize the battery grouping topology with the objective of minimizing the total cost of ownership (TCO. The battery capacity and the serial and parallel amounts of the pack can thus be determined considering the influence of battery degradation. The results show that the optimized pack grouping can be solved by GA within around 9 min. Compared with the results of maximum discharge efficiency within a fixed lifetime, the proposed method can not only achieve a higher discharge efficiency, but also reduce the TCO by 2.29%. To enlarge the applications of the proposed method, the sensitivity to driving conditions is also analyzed to further prove the feasibility of the proposed method.

  6. Forecaster priorities for improving probabilistic flood forecasts

    Science.gov (United States)

    Wetterhall, Fredrik; Pappenberger, Florian; Alfieri, Lorenzo; Cloke, Hannah; Thielen, Jutta

    2014-05-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  7. Respiration monitoring by Electrical Bioimpedance (EBI) Technique in a group of healthy males. Calibration equations.

    Science.gov (United States)

    Balleza, M.; Vargas, M.; Kashina, S.; Huerta, M. R.; Delgadillo, I.; Moreno, G.

    2017-01-01

    Several research groups have proposed the electrical impedance tomography (EIT) in order to analyse lung ventilation. With the use of 16 electrodes, the EIT is capable to obtain a set of transversal section images of thorax. In previous works, we have obtained an alternating signal in terms of impedance corresponding to respiration from EIT images. Then, in order to transform those impedance changes into a measurable volume signal a set of calibration equations has been obtained. However, EIT technique is still expensive to attend outpatients in basics hospitals. For that reason, we propose the use of electrical bioimpedance (EBI) technique to monitor respiration behaviour. The aim of this study was to obtain a set of calibration equations to transform EBI impedance changes determined at 4 different frequencies into a measurable volume signal. In this study a group of 8 healthy males was assessed. From obtained results, a high mathematical adjustment in the group calibrations equations was evidenced. Then, the volume determinations obtained by EBI were compared with those obtained by our gold standard. Therefore, despite EBI does not provide a complete information about impedance vectors of lung compared with EIT, it is possible to monitor the respiration.

  8. 提高月度售电量预测精度的一种新方法%A new method to promote the forecasting accuracy of monthly electricity consumption

    Institute of Scientific and Technical Information of China (English)

    潘小辉; 刘丽萍; 李扬

    2013-01-01

      This paper finds the principles and characters of the development of electricity consumption by making data mining analysis on historical data, and puts forward a new method to fore⁃cast monthly electricity consumption. When forecasting the elec⁃tricity consumption of a month, the new method firstly forecasts the electricity consumption of the season that the month belongs to. Then use the Forecasting values of the season electricity consump⁃tion and season proportion to forecast the monthly electricity con⁃sumption. The new method has been applied to forecast monthly electricity consumption from April of 2005 to December of 2007 in a city of Jiangsu province. With 2.35% average relative error, the results show the validity of the new method.%  通过对地区售电量历史数据的研究分析,挖掘出其发展的规律和特点,并创新性地提出了一种月度售电量预测的新方法。该方法在预测某月售电量时,先预测出该月所在季度的季度售电量,再根据占季比和所在季度的季度售电量的预测值,预测出该月的售电量,若该月处于春节影响期将最后给予修正。利用该预测新方法对江苏某市2005年4月至2007年12月的月度售电量进行了预测,预测的平均相对误差为2.35%,预测精度得到了较大的提高,证明了该预测新方法的准确性和可靠性。

  9. Forecasts for the electric energy supply in the Brazilian South-Southeast market 1990-2000; Perspectivas para o atendimento de mercado de energia eletrica Sul-Sudeste, 1990-2000

    Energy Technology Data Exchange (ETDEWEB)

    Ramos, Dorel Soares [Companhia Energetica de Sao Paulo, SP (Brazil)]|[Sao Paulo Univ., SP (Brazil). Escola Politecnica; Mazzon, Joao Gilberto [Companhia Energetica de Sao Paulo, SP (Brazil)

    1990-05-01

    In opposite to the recent expectations, the systematic delays in the operational start of the constructions of new electric power generation units in south and southeast regions of Brazil, caused by financial restrictions, will not lead to energy rationalization. This fact reached out from the low expansion rates of the consumer sector in this regions of Brazil. This paper also shows the demand factors of this market and forecast the future electric energy market for these regions until the years 2000. 3 figs., 1 tab.

  10. Incorporating single molecules into electrical circuits. The role of the chemical anchoring group.

    Science.gov (United States)

    Leary, Edmund; La Rosa, Andrea; González, M Teresa; Rubio-Bollinger, Gabino; Agraït, Nicolás; Martín, Nazario

    2015-02-21

    Constructing electronic circuits containing singly wired molecules is at the frontier of electrical device miniaturisation. When a molecule is wired between a pair of electrodes, the two points of contact are determined by the chemical anchoring groups, located at the ends of the molecule. At this point, when a bias is applied, electrons are channelled from a metallic environment through an extremely narrow constriction, essentially a single atom, into the molecule. The fact that this is such an abrupt change in the electron pathway makes the nature of the chemical anchoring groups critically important regarding the propagation of electrons from the electrode across the molecule. A delicate interplay of phenomena can occur when a molecule binds to the electrodes, which can produce profound differences in conductance properties depending on the anchoring group. This makes answering the question "what is the best anchoring group for single molecule studies" far from straight forward. In this review, we firstly take a look at techniques developed to 'wire-up' single molecules, as understanding their limitations is key when assessing a molecular wire's performance. We then analyse the various chemical anchoring groups, and discuss their merits and disadvantages. Finally we discuss some theoretical concepts of molecular junctions to understand how transport is affected by the nature of the chemical anchor group.

  11. Short time demand forecasting in electric power system using ITSM software package; Zastosowanie pakietu ITSM do krotkoterminowej prognozy mocy w systemie energetycznym

    Energy Technology Data Exchange (ETDEWEB)

    Mazurek, P. [Instytut Automatyki Systemow Energetycznych, Wroclaw (Poland)

    1995-07-01

    Application of the ITSM software package is presented together with the used iterative time series modeling methodology. The results obtained for the National Power System 24 hour load forecast were calculated for 200 days with acceptable accuracy. A time required for input data analysis and single forecast calculations was approx. 20 minutes on standard IBM PC. (author). 5 refs., 4 figs., 2 tabs.

  12. Innovation Forecasting

    Science.gov (United States)

    1997-11-01

    relating to “ injectors ”) to develop a map of the related technologies [33.] Another approach is to develop a “tree” showing a system branching into its...additional terms such as “trend,” “forecast,” “ delphi ,” “assessment,” and so forth may call up other forecasts and assessments relating to the topic...present and future engine technologies. A preliminary search (Step 1, Table 5) located prior forecasts, in particular, a Delphi study [36]. The Delphi

  13. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

  14. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  15. Electric properties and fabrication of IMI-O LB films containing the imidazole group

    CERN Document Server

    Yoo, S Y; Kwon, Y S; Park, J C

    1999-01-01

    We fabricated an IMI-O polymer containing an imidazole group that could form a complex structure between the monolayer and the metal ions at the air-water interface. Also, the monolayer behavior at the air-water interface and the electrical properties of metal-complexed Langmuir-Blodgett (LB) films were investigated by using Brewster angle microscopy (BAM) and current-voltage(I-V) measurements. The difference in the BAM images between the pure water and the aqueous metal ions is attributed to the interactions of the copolymers with the metal ions at the interface and the consequent change of the monolayer organization. In the I-V characteristics, the current for LB films with different metal ion depended on the quantity of the metal-ion complexed with the LB film due to the interaction between the metal ion and the IMI-O polymer.

  16. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  17. Research on Forecasting Method of Whole Society Electricity Consumption Based on Business Expansion%基于业扩报装的全社会电量预测方法研究

    Institute of Scientific and Technical Information of China (English)

    葛斐; 李周; 杨欣; 荣秀婷; 李凯; 石雪梅

    2013-01-01

    The capacity of business expansion from power system demand side is an important reference index to analyze and forecast the whole society electricity consumption .In the past years , the researches mainly have focused on qualitative analysis .In this paper , taking Anhui province as an example , on the basis of seasonal decomposition for the related variables , the model between the capacity of business ex-pansion and the whole society electricity consumption for month is built .The result shows that the predic-ting result of the model has better reference significance , and applied to forecast the short-term monthly e-lectricity consumption .%电力系统需求侧业扩报装的容量是分析预测全社会用电量的一个重要参考指标,长期以来,都以定性分析为主,文章以安徽为例,在对相关变量进行季节性分解的基础上,建立业扩报装容量和全省全社会用电量之间的定量关系月度模型。结果表明所建模型预测结果具有较高参考意义,并适用于短期月度电量预测。

  18. Can Business Students Forecast Their Own Grade?

    Science.gov (United States)

    Hossain, Belayet; Tsigaris, Panagiotis

    2013-01-01

    This study examines grade expectations of two groups of business students for their final course mark. We separate students that are on average "better" forecasters on the basis of them not making significant forecast errors during the semester from those students that are poor forecasters of their final grade. We find that the better…

  19. Beat the Instructor: An Introductory Forecasting Game

    Science.gov (United States)

    Snider, Brent R.; Eliasson, Janice B.

    2013-01-01

    This teaching brief describes a 30-minute game where student groups compete in-class in an introductory time-series forecasting exercise. The students are challenged to "beat the instructor" who competes using forecasting techniques that will be subsequently taught. All forecasts are graphed prior to revealing the randomly generated…

  20. Beat the Instructor: An Introductory Forecasting Game

    Science.gov (United States)

    Snider, Brent R.; Eliasson, Janice B.

    2013-01-01

    This teaching brief describes a 30-minute game where student groups compete in-class in an introductory time-series forecasting exercise. The students are challenged to "beat the instructor" who competes using forecasting techniques that will be subsequently taught. All forecasts are graphed prior to revealing the randomly generated…

  1. Novel grey forecast model and its application

    Institute of Scientific and Technical Information of China (English)

    丁洪发; 舒双焰; 段献忠

    2003-01-01

    The advancement of grey system theory provides an effective analytic tool for power system load fore-cast. All kinds of presently available grey forecast models can be well used to deal with the short-term load fore-cast. However, they make big errors for medium or long-term load forecasts, and the load that does not satisfythe approximate exponential increasing law in particular. A novel grey forecast model that is capable of distin-guishing the increasing law of load is adopted to forecast electric power consumption (EPC) of Shanghai. Theresults show that this model can be used to greatly improve the forecast precision of EPC for a secondary industryor the whole society.

  2. 基于模糊C均值聚类的居民用户中期用电需求预测模型%Residents mid-term electricity demand forecasting model based on fuzzy C-means clustering

    Institute of Scientific and Technical Information of China (English)

    蔡秀雯; 杨加生

    2016-01-01

    随着智能电网的加快建设和居民阶梯电价机制的实施,居民用电需求预测变得更加复杂。从现行居民阶梯电价机制下用户动态需求的特征出发,提出了一个新的基于模糊C均值聚类的居民用电需求分类预测模型。通过模糊C均值聚类对某地区居民用电行为进行聚类分析、数据分类,建立基于自组织模糊神经网络的用电需求预测模型,提高了中期用电需求预测精度。%With speeding up of smart grid construction and the implementation of the residents step tariff mechanism,residen⁃tial electricity demand forecasting has become more complicated. From the current residents step tariff mechanism and according to the characteristics of dynamic demand, this paper has proposed a new fuzzy C⁃means clustering based on the classification of resi⁃dential electricity demand forecasting model. By fuzzy C⁃means clustering for residential electricity behavior in order to clustering analysis and data classification,this paper has established electrici⁃ty demand forecasting model based on the self⁃organizing fuzzy neural network ,and has improved the medium⁃term demand fore⁃cast accuracy.

  3. 基于果蝇优化灰色神经网络的年电力负荷预测%Annual Electric Load Forecasting Based on Gray Neural Network with Fruit Fly Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    傅军栋; 刘晶; 喻勇

    2015-01-01

    The accuracy of annual electric load forecasting plays an important role in economic and social benefits of electric power systems. The Gray Neural Network is an innovative computing approach, which has found wide application in reality. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a GNN-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the parameters for the GNN model to improve the forecasting accuracy and stability of the model. By taking the annual electricity consumption of China as an in⁃stance, the computational result shows that the GNN combined with FOA outperforms other alternative methods, namely the single GNN, the generalized regression neural network, the least squares support vector machine (LSSUM) and the regression model.%年电力负荷预测的准确性对电力系统的经济效益和社会效益具有重要作用。灰色神经网络(GNN)是一种创新的智能计算方法,在实际中广泛应用。尤其在预测问题方面具有极大的潜力。作为一种新型的启发式和进化算法,果蝇优化算法(FOA)具有易理解和快速收敛到全局最优解的优点。为提高预测性能,提出一种以GNN为基础的年电力负荷预测模型,使用FOA自动确定GNN模型的相应参数值,提高模型的稳定性和预测精度。通过利用中国的年用电量为实例,计算结果表明,GNN结合FOA(GNN-FOA)优于GNN ,广义回归神经网络(GRNN),最小二乘支持向量机(LSSVM)和回归模型等其他替代方法。

  4. A strategy for short-term load forecasting in Ireland

    OpenAIRE

    Fay, Damien

    2004-01-01

    Electric utilities require short-term forecasts of electricity demand (load) in order to schedule generating plant up to several days ahead on an hourly basis. Errors in the forecasts may lead to generation plant operation that is not required or sub-optimal scheduling of generation plants. In addition, with the introduction of the Electricity Regulation Act 1999, a deregulated market structure has been introduced, adding increased impetus to reducing forecast error and the associated costs. ...

  5. Uses and Applications of Climate Forecasts for Power Utilities.

    Science.gov (United States)

    Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David

    1995-05-01

    The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector.

  6. Forecast of geothermal-drilling activity

    Energy Technology Data Exchange (ETDEWEB)

    Mansure, A.J.; Brown, G.L.

    1982-07-01

    The number of geothermal wells that will be drilled to support electric power production in the United States through 2000 A.D. are forecasted. Results of the forecast are presented by 5-year periods for the five most significant geothermal resources.

  7. General forecasting correcting formula

    OpenAIRE

    Harin, Alexander

    2009-01-01

    A general forecasting correcting formula, as a framework for long-use and standardized forecasts, is created. The formula provides new forecasting resources and new possibilities for expansion of forecasting including economic forecasting into the areas of municipal needs, middle-size and small-size business and, even, to individual forecasting.

  8. General forecasting correcting formula

    OpenAIRE

    2009-01-01

    A general forecasting correcting formula, as a framework for long-use and standardized forecasts, is created. The formula provides new forecasting resources and new possibilities for expansion of forecasting including economic forecasting into the areas of municipal needs, middle-size and small-size business and, even, to individual forecasting.

  9. Information Forecasting.

    Science.gov (United States)

    Hanneman, Gerhard J.

    Information forecasting provides a means of anticipating future message needs of a society or predicting the necessary types of information that will allow smooth social functioning. Periods of unrest and uncertainty in societies contribute to "societal information overload," whereby an abundance of information channels can create communication…

  10. 基于多因素加法模型的中期电力负荷预测%Multiple Factors Addictive Model for Mid-Term Electric Load Forecasting

    Institute of Scientific and Technical Information of China (English)

    翁金芳; 黄伟; 江育娥; 林劼

    2016-01-01

    提前准确预测所需电力负荷,做好电力规划是电力部门保证电力供应稳定不可或缺的重要环节.基于欧洲智能网络(EUNITE)竞赛电力数据和北美电力数据,提出一种多因素加法模型,进行中期电力预测.考虑到温度、假期、星期等因素对电力负荷产生不同的影响,拟合出这些因素与电力负荷之间的映射关系,相加得到电力负荷预测的函数.还比较了业界常用的7种不同的算法模型,使用6种不同指标对这些模型和多因素加法模型进行评估,实验结果发现,在这8种不同算法模型中,多因素加法模型有着更加精确的预测性能,运算速度比其他模型快,并且模型更加容易理解和解释.%Accuracy forecasting of electric load is important for power system to make plan. A Multiple Factors Addictive (MFA) model is proposed to predict mid-term electric load based on Europe (EUNITE) competition dataset and North American electric dataset. Firstly, MFA considers factors such as temperature, holiday, and week separately to fit functions for electric loads. And then all these fitted functions are added together to a unified function, which is used to make prediction of the electric load. Seven other state-of-art algorithms which are popular in the field are also used to make forecasting. The performances of prediction models are evaluated by using 6 different metrics. Compared with 7 other kinds of different models prediction results, MFA has the advantages of more accurate forecasting performance and faster operational speed, and is simple and easy to understand.

  11. HYBRID AND INTEGRATED APPROACH TO SHORT TERM LOAD FORECASTING

    Directory of Open Access Journals (Sweden)

    J. P. Rothe,

    2010-12-01

    Full Text Available The forecasting of electricity demand has become one of the major research fields in Electrical Engineering. In recent years, much research has been carried out on the application of artificial intelligence techniques to the Load-Forecasting problem. Various Artificial Intelligence (AI techniques used for load forecasting are Expert systems, Fuzzy, Genetic Algorithm, Artificial Neural Network (ANN. This research work is an attempt to apply hybrid and ntegrated effort to forecast load. Regression, Fuzzy and Neural alongwith Genetic Algorithm will empower the analysts to strongly forecast fairly accurate load demand on hourly base.

  12. Pseudo-random renormalization group forward and inverse modeling of the electrical properties of some carbonate rocks

    Science.gov (United States)

    Gomaa, Mohamed M.; Kassab, Mohamed A.

    2016-12-01

    Electrical properties of carbonate rocks from North Sinai, Egypt, were investigated experimentally in the frequency domain (100 Hz to 100 kHz). Changes between electrical properties were attributed to changes in mineral composition and texture of samples. Asymmetric mixture laws cannot describe electrical behavior of heterogeneous rocks in the mentioned frequency range. A theoretical pseudo-random renormalization group (PRNG) method was developed to model electrical behavior of rock mixtures. The main goal of this paper is to make forward and inverse modeling using PRNG method for the electrical properties as a function of frequency for carbonate rocks with texture. In PRNG method four phases were used to take into account the texture in the samples. Four phases are the best that we could use in the model. We are trying to increase the competence of the model in the near future. In these four phases mainly conducting constituents (silt and clay) and mainly insulating constituents (sand, air and carbonate) may coat each other. With appropriately chosen coatings for the four phases, the PRNG method can reasonably model the electrical properties of the samples in the measured frequency range. Thus, an inverse problem is used to find the detailed structure of the four phases, which leads to the measured electrical properties of the samples, taking into account the measured concentrations of the different constituents of the samples. The inverse problem is based on minimizing the misfit between the measured frequency response for the conductivity and the dielectric constant and those obtained theoretically from the PRNG method. It was shown that the texture plays an important role in determining the A.C. electrical properties of heterogeneous samples.

  13. Requirements and benefits of flow forecasting for improving hydropower generation

    OpenAIRE

    Dong, Xiaohua; Vrijling, J. K.; Dohmen-Janssen, Catarine M.; Ruigh, E.; Booij, Martijn J.; Stalenberg, B.; Hulscher, Suzanne J.M.H.; Van Gelder, P.H.A.J.M.; Verlaan, M.; Zijderveld, A; Waarts, P.

    2005-01-01

    This paper presents a methodology to identify the required lead time and accuracy of flow forecasting for improving hydropower generation of a reservoir, by simulating the benefits (in terms of electricity generated) obtained from the forecasting with varying lead times and accuracies. The benefit-lead time relationship was investigated only for perfect inflow forecasts, with a few selected forecasting lead times: 4, 10 days and 1 year. The water level and the release from the reservoir were ...

  14. Interagency Advanced Power Group, Joint Electrical and Nuclear Working Group, meeting minutes, November 16--17, 1993

    Energy Technology Data Exchange (ETDEWEB)

    1993-12-31

    Reports on soldier power R&D review, N-MCT power electronic building blocks, silicon carbide power semiconductor work, and ground based radar were made to the Power Conditioning Panel. An introduction to high temperature electronics needs, research and development was made to the High Temperature Electronics Subcommittee. The Pulse Power Panel received reports on the navy ETC gun, and army pulse power. The Superconductivity Panel received reports on high-tc superconducting wires, superconducting magnetic energy storage, and superconducting applications. The Nuclear Working Group received presentations on the Topaz nuclear power program, and space nuclear work in the Department of Energy.

  15. Electric energy demand and supply prospects for California

    Science.gov (United States)

    Jones, H. G. M.

    1978-01-01

    A recent history of electricity forecasting in California is given. Dealing with forecasts and regulatory uncertainty is discussed. Graphs are presented for: (1) Los Angeles Department of Water and Power and Pacific Gas and Electric present and projected reserve margins; (2) California electricity peak demand forecast; and (3) California electricity production.

  16. Politics of energy forecasting. A comparative study of energy forecasting in Western Europe and North America

    Energy Technology Data Exchange (ETDEWEB)

    Baumgartner, T.; Midttun, A. (eds.)

    1987-01-01

    The comprehensive book presented covers the theory and praxis of energy modelling, pitfalls of mega-modelling and negotiated futures eg. energy forecasting in West Germany. Elites and professionals, electricity forecasting in Norway, Denmark and France, and market competition are reviewed, and comparative notes on politics and methodology are given.

  17. 大数据背景下的充电站负荷预测方法%Load Forecasting Method for Electric Vehicle Charging Station Based on Big Data

    Institute of Scientific and Technical Information of China (English)

    黄小庆; 陈颉; 陈永新; 杨夯; 曹一家; 江磊

    2016-01-01

    The load forecast of electric vehicles (EVs) is the foundation of planning and scheduling of charging stations. Compared with the traditional method,the load forecast method under big data has the feature that the data to be forecast is quickly observable,real time,etc.Hence the need of the corresponding adjustments of the load forecast methods.This paper first analyzes the data demand for charging station planning and scheduling,and then the ways of main data acquisition.Based on volume,variety,velocity data,each EV”s start time,duration and location for charging,it will be possible to build the load model of a single EV.Furthermore,the total charging power of a charging station can be estimated by origin-destination(OD) flow statistics or adding up all the EV loads that are connected with its related transport line and node.Finally,a case study is given around the load forecast of EV station,and the load forecasting results from different load forecasting methods are compared.%电动汽车负荷预测是充电站规划及调度的研究基础。相比传统的负荷预测,大数据背景下的负荷预测具有待预测数据可快速观测的特点,此时负荷预测方法需要相应调整。首先分析了充电站负荷预测所需数据及主要数据来源。其次,针对单辆电动汽车,基于大量、快速更新、多种类的数据分析电动汽车的充电习惯,预测每一辆电动汽车的充电开始时间、持续时间和充电地点,获取单辆电动汽车的负荷模型。该模型综合考虑电池状态、出行时间、行驶路径与速度、充电偏好等信息。然后,面向任意充电站,对与其相关的路网节点与交通线路上的所有电动汽车负荷求和,估算该充电站的总充电功率。最后,进行实例仿真,并与传统方法下的充电负荷预测结果进行了对比。

  18. FORECASTING NEW PRODUCT SALES

    Directory of Open Access Journals (Sweden)

    R. Siriram

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: This paper tests the accuracy of using Linear regression, Logistics regression, and Bass curves in selected new product rollouts, based on sales data. The selected new products come from the electronics and electrical engineering and information and communications technology industries. The eight selected products are: electronic switchgear, electric motors, supervisory control and data acquisition systems, programmable logic controllers, cell phones, wireless modules, routers, and antennas. We compare the Linear regression, Logistics regression and Bass curves with respect to forecasting using analysis of variance. The accuracy of these three curves is studied and conclusions are drawn. We use an expert panel to compare the different curves and provide lessons for managers to improve forecasting new product sales. In addition, comparison between the two industries is drawn, and areas for further research are indicated.

    AFRIKAANSE OPSOMMING: Hierdie artikel toets die akkuraatheid van die gebruik van linêere regressie, logistiese regressie en Bass-krommes by die bekendstelling van nuwe produkte gebaseer op verkoopsdata. Die geselekteerde nuwe produkte is uit die elektriese en elektroniese asook informasietegnologie- en kommunikasie bedrywe. Linêere regressie, logistiese regressie en Bass-krommes word vergelyk ten opsigte van vooruitskatting deur variansie te ontleed. Die akkuraatheid word ontleed en gevolgtrekkings gemaak. Die doel is om vooruitskatting van nuwe produkverkope te verbeter.

  19. kwmc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kont Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. kcrg Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. kjac Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. krdu Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. kiwd Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. krbl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. kssf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. ksaw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. kmot Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. kiso Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. kgck Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. kcvg Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. pafa Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kcrq Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. ksun Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kpia Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. krow Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kbtv Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kbke Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. kbpt Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kact Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. kavl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. kbur Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. krsw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. klnd Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. kpuw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. kbis Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kcmx Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. kipt Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. kteb Terminal Aerodrome Forecast

    Data.gov (United States)

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    Data.gov (United States)

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  11. khon Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. kpeq Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kden Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kmia Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kmaf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. khsa Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kvis Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. ktcl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. kmrb Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kcub Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...