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Short term load forecasting for Iran national power system using artificial neural network and fuzzy expert system

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4 Author(s)
Ansarimehr, P. ; Dept. of Power Syst. Oper., Niroo Res. Inst., Tehran, Iran ; Barghinia, S. ; Habibi, H. ; Vafadar, N.

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.

Published in:

Power System Technology, 2002. Proceedings. PowerCon 2002. International Conference on  (Volume:2 )

Date of Conference:

2002