Hybrid and integrated intelligent system for load demand prediction

Abstract

Artificial neural networks (ANN) are receiving a lot of attention because of their nonlinear mapping ability in the field of short term load forecast (STLF). ANN based STLF model commonly use back propagation algorithm, that may not converge properly, that affects the forecast accuracy. A hybrid approach, based on artificial neural network (ANN) and genetic algorithm (GA) that combines the advantages of each technique is proposed in this research. Genetic algorithm is implemented for the optimization of the architecture of feedforward neural network and selection of its initial weight values. Error back propagation algorithm for the training of the optimized neural network will be implemented. The second stage of this research is related with the complete training of the neural network based on genetic algorithm, using genetic manipulation of chromosomes. The results show that this approach produced better output in terms of enhanced forecast accuracy.