Annual Report 2003-2004

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NRIElectric Power Systems Research CenterProject Title:Application Software for Short Term Load Forecasting UsingArtificial Neural Networks and Fuzzy Expert SystemDepartment: Power Systems Operation Project Manager: S. BarghiniaEmployer: Niroo Research Institute Project Code: PONPN03Project Staff: P. Ansarimehr, H. Habibi, N. VafadarSummary:One of the most important requirements for operation and planning activities of anelectrical utility is the prediction of load for the next hour to several days out, known asshort term load forecasting (STLF). In the software, ANNs are trained with the loadpatterns corresponding to the forecasting hours, and the forecasted load is obtained.Considering the load variations of the lunar and solar holidays, the FES modifies theresults of ANN for these days. The program has satisfactory results with about 2 percenterror for one hour up to a week prediction of Iran national power system (INPS) Load.Following the official notice of regulation for pricing and buying/selling conditions ofelectrical energy and also the opening of the electrical energy market in Iran, theregional electric companies should be able to predict their hourly load consumption forone hour up to a few days. With a few changes, the NSTLF II software can be used forthis purpose.Main Menu of the NSTLF II16
Electric Power Systems Research CenterNRIProject Results: Application Software for short term load forecasting using artificail neuralnetworks and fuzzy expert system. Data entry and implementation for INPS with 2% meen percentage error.Project Documentation:- Power Systems Operation Department; Report of “Considerations in the Short TermLaod Forecasting Using Artificial Intelligent Methods”; Electric Power SystemsResearch Center; NRI.- Power Systems Operation Department; Report of “Sensitivity Analysis of Load withrelation to Effective Parameters”; Electric Power Systems Research Center; NRI.- Power Systems Operation Department; Report of “Short Term Load ForecastingUsing Artificial Intelligent Methods”; Electric Power Systems Research Center; NRI.17
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Annual Report 2003-2004

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