Forecasting Electric Load by Support Vector Machines with Genetic Algorithms

Abstract

Support vector machines (SVMs) have been successfully used in solving nonlinear regression and time series problems. However, the application of SVMs to load forecasting is very rare. Therefore, the purpose of this paper is to examine the feasibility of SVMs in forecasting electric load. In addition, the genetic algorithms are applied in the parameter selection of SVM model. Forecasting results compared with other two models, namely autoregressive integrated moving average (ARIMA) and general regression neural networks (GRNN), are provided. The experimental data are borrowed from the Taiwan Power Company. The numerical results indicate that the SVM model with genetic algorithms (SVMG) results in better predictive performance than the other two approaches.