New Momentum Adjustment Technique for Levenberg -Marquardt Neural Network Training Used in Short Term Load Forecasting  Fulltext 

[ Khosravi.Z ] - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN
[ Barghinia ] - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN
[ Ansarimehr ] - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN

خلاصه مقاله:

One of the important requirements for operational planning of electrical utilities and also transactions of electrical power markets is the prediction of hourly load up to several days, known as short term load forecasting (STLF). Nowadays, intelligent methods, specially, artificial neural network (ANN) is the dominant method when it comes to STLF. The Levenberg-Marquardt (LM) algorithm has been extensively used as training method for ANNs. In this work, a new momentum adjustment technique is implemented for training ANN of Iran national power system (INPS) STLF. The performance is compared with conventional LM algorithm with other momentum adjustment techniques. The new method of momentum adjustment for LM algorithm improves learning of ANN for STLF of INPS in the sense of error and time consumption.

Short Term Load Forecasting, Artificial Neural Networks (ANN), Levenberg-Marquardt (LM) Algorithm