Title :
Short term load forecasting for Iran national power system using artificial neural network and fuzzy expert system
Author :
Ansarimehr, P. ; Barghinia, S. ; Habibi, H. ; Vafadar, N.
Author_Institution :
Dept. of Power Syst. Oper., Niroo Res. Inst., Tehran, Iran
Abstract :
One of the requirements for the operation and planning activities of an electrical utility is the prediction of load for the next hour to several days out, known as short term load forecasting (STLF). This paper presents the STLF of the Iranian national power system (INPS) using artificial neural networks (ANN) and fuzzy expert systems (FES). The ANN is trained with the load patterns corresponding to the forecasting hours and the forecasted load is obtained. The FES modifies the initial forecasted load for the special holidays and also in the case sudden changes in temperature. A data analyser and a temperature forecaster are also included in the NRI STLF (NSTLF) package. The program has satisfactory results for one hour up to a week prediction of INPS load.
Keywords :
expert systems; fuzzy neural nets; load forecasting; power system analysis computing; Iran; Levenberg-Marquardt method; artificial neural network; data analyser; fuzzy expert system; planning activities; short term load forecasting; temperature forecaster; Artificial neural networks; Casting; Data analysis; Hybrid intelligent systems; Indium phosphide; Load forecasting; Power system planning; Power systems; Temperature; Weather forecasting;
Conference_Titel :
Power System Technology, 2002. Proceedings. PowerCon 2002. International Conference on
Print_ISBN :
0-7803-7459-2
DOI :
10.1109/ICPST.2002.1047567