Abstract
Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is a novel and effective regression algorithm, is applied to grid resources prediction. In order to build an effective SVR model, SVR’s parameters must be selected carefully. Therefore, we develop a simulated annealing algorithm-based SVR (SA-SVR) model that can automatically determine the optimal parameters of SVR with higher predictive accuracy and generalization ability simultaneously. The performance of the hybrid model (SA-SVR), the back-propagation neural network (BPNN) and traditional SVR model whose parameters are obtained by trial-and-error procedure (T-SVR) have been compared with benchmark data set. Experimental results demonstrate that SA-SVR model works better than the other two models.
Highlights
Thereafter, the prediction models of SA-support vector regression (SVR) and T-SVR were built with the selected parameters
Accurate grid resources prediction is crucial for a grid scheduler
It means that SA is applied to SVR’s parameters selection successfully
Summary
2005) have been developed and have generated accurate prediction in practice These prediction models that provide future resources information generally apply the time series prediction models which mostly use statistical and artificial intelligent approach. O'Hallaron., 1999) is a project in which grid resources are modeled as linear time series process. Multiple conventional linear models are evaluated, including AR, MA, ARMA, ARIMA and ARFIMA models Their results show that the simple AR model is the best model of this class because of its good predictive power and low overhead. 1999) uses a combination of several models for the prediction of one resource. With the development of artificial neural networks (ANNs), ANNs have been successfully employed for modeling time series. 2005) applied ANNs to grid resources prediction successfully.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
