Building an integrated hybrid model for short-term and mid-term load forecasting with genetic optimization

The enhancement of load forecasting has become one of the core research topics in the energy field. Because power load has both time-variant and nonlinear characteristics, different types of methods, neural networks (NN) in particular, have been applied to power load forecasting. This study proposes a real-valued genetic algorithm (RGA)- based neural network with support vector machine (NN-SVM) model to predict the power load in both short-term and mid-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and RGA. The model consists of two stages. In short-term load forecasting (STLF), the first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast. Similar operations are used in mid-term load forecasting (MTLF). In the process of SVM training and NN learning, RGA is used to find the optimal parameters. The results of several experiments show that this new model performs more accurately and stably than some conventional models including RBFNN, RGA-SVM, Karman filter in STLF. Also it is able to function well in MTLF.