Analysis of effective variables on daily electrical load curves of Iran power network

In the presented paper by analyzing the curve of the daily electrical network load in Iran over a 10 year period; the effective factors on the daily electricity consumption (including time, environmental and special factors) are studied. Additionally , using the final results from this graphical analysis, a suitable method to train artificial neural networks for short-term forecasting of the time of Iranpsilas electrical network consumption have been presented.

[1]  P. McSharry,et al.  Probabilistic forecasts of the magnitude and timing of peak electricity demand , 2005, IEEE Transactions on Power Systems.

[2]  A. Torres,et al.  Soft computing techniques for short term load forecasting , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[3]  Herbert Müller,et al.  Energieprognose Mittels Neuraler Netzwerkkonzepte , 1995 .

[4]  N.D. Hatziargyriou,et al.  An optimized adaptive neural network for annual midterm energy forecasting , 2006, IEEE Transactions on Power Systems.

[5]  M. Farhadi,et al.  A Novel Model for Short Term Load Forecasting of Iran Power Network by Using Kohonen Neural Networks , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[6]  Tomonobu Senjyu,et al.  Next day load curve forecasting using recurrent neural network structure , 2004 .

[7]  S.M.M. Tafreshi,et al.  Improved SOM based method for short term load forecast of Iran power network , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[8]  N. Amjady,et al.  Short-Term Bus Load Forecasting of Power Systems by a New Hybrid Method , 2007, IEEE Transactions on Power Systems.