Predicting Grid Resource using SVM and Simulated Annealing Algorithms

Abstract

SVM and Simulated Annealing Algorithms were applied to grid resources prediction. In order to build an effective SVR model, the parameters must be selected with attention. Here, a simulated annealing algorithm-based SVR (SA-SVR) model has been developed to determine the optimal parameters of SVR. The performance of the hybrid model, the back-propagation neural network and traditional SVR model whose parameters are provided by trial-and-error procedure have been compared with benchmark data set. Experiments validate that SA-SVR model works better than the other two models.