Grid Resources Prediction with Support Vector Regression and Particle Swarm Optimization

Abstract

Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resource prediction. In order to obtain better prediction performance, SVR’s parameters must be selected carefully. Therefore, a particle swarm optimization-based SVR (PSO-SVR) model, in which PSO is used to determine free parameters of SVR, is presented in this study. The hybrid model (PSO-SVR) can automatically determine the parameters of SVR with higher predictive accuracy and generalization ability simultaneously. The performance of PSO-SVR, the back-propagation neural network (BPNN) and the traditional SVR model whose parameters are obtained by trail-and-error procedure (T-SVR) have been compared with benchmark data set. Experimental results indicate that the PSO-SVR model can achieve higher predictive accuracy than the other two models.