Short term load forecasting based on hybrid ANN and PSO

Abstract

Short term load forecasting (STLF) is the prediction of electrical load for a period that ranges from one hour to a week. The main objectives of the (STLF) are to predict future load for the generation scheduling at power stations; assess the security of the power system as well as for timely dispatching of electrical power. The traditional load forecasting tools utilize time series models which extrapolate historical load data to predict the future loads. These tools assume a static load series and retain normal distribution characteristics. Due to their inability to adapt to changing environments and load characteristics, they often lead to large forecasting errors. In an effort to reduce the forecasting error, hybrid artificial neural network (ANN) and particle swarm optimization (PSO) is used in this paper. It is shown that the hybridization of ANN and PSO gives better results compared to the standard ANN with back propagation.