Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms

The empirical results reveal that the proposed RSVMG model outperforms the SVM model, artificial neural network (ANN) model and regression model and provides a promising alternative for forecasting electricity load in power industry.

Abstract

Accompanying deregulation of electricity industry, accurate load forecasting of the future electricity demand has been the most important role in regional or national power system strategy management. Electricity load forecasting is complex to conduct due to its nonlinearity of influenced factors. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. However, the application for load forecasting is rare. In this study, a recurrent support vector machines with genetic algorithms (RSVMG) is proposed to forecast electricity load. In addition, genetic algorithms (GAs) are used to determine free parameters of support vector machines. Subsequently, examples of electricity load data from Taiwan are used to illustrate the performance of proposed RSVMG model. The empirical results reveal that the proposed model outperforms the SVM model, artificial neural network (ANN) model and regression model. Consequently, the RSVMG model provides a promising alternative for forecasting electricity load in power industry.