Dynamic neural network based genetic algorithm optimizing for short term load forecasting

Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network based genetic algorithm (GA) optimizing to develop the accuracy of predictions. With GA´s optimizing and BP neural network´s dynamic feature, the weight optimization is realized by selection, crossing and mutation operations. Using load time series from a practical power system, we tested the performance of BP neural network based genetic algorithm optimizing by comparing its predictions with that of BP network.