Short term load forecasting of Iran national power system using artificial neural network generation two

This paper presents a neuro-based short term load forecasting (STLF) method for Iran national power system (INPS) and its regions. This is an improved version of the one given in [1]. The architecture of the proposed network is a three-layer feed forward neural network whose parameters are tuned by Levenberg-Marquardt BP (LMBP) augmented by an early stopping (ES) method tried out for increasing the speed of convergence. Instead of seasonal training, an input as a month indicator is added to the input vectors. The short term load forecasting simulator developed so far presents satisfactory and better results for one hour up to a week prediction of INPS loads and region of INPS, Bakhtar Region Electric Co (BREC).