Support Vector Machine Optimized with Genetic Algorithm for Short-Term Load Forecasting

Abstract

Accurately electric load forecasting has become the most important management goal, however, electric load often presents nonlinear data patterns. Therefore, a rigid forecasting approach with strong general nonlinear mapping capabilities is essential. In recent few decades, support vector machines (SVM) has been successfully employed to solve this problem. This paper elucidates the feasibility of using SVM to forecast electricity load. Moreover, genetic algorithms (GA) were employed to choose the parameters of a SVM model. So, a GA-SVM model for short-term load forecasting is presented in this paper. The experiment results show the method in the paper has greater improvement in both accuracy and velocity of convergence for SVM. Consequently, the model is practical and effective and provides a alternative for forecasting electricity load.