Load prediction using hybrid model for computational grid
Yongwei Wu, Yulai Yuan, Guangwen Yang, Weimin Zheng · 2007
Due to the dynamic nature of grid environments, schedule algorithms always need assistance of a long-time-ahead load prediction to make decisions on how to use grid resources efficiently. In this paper, we present and evaluate a new hybrid model, which predicts then-step-ahead load status by using interval values. This model integrates autoregressive (AR) model with confidence interval estimations to forecast the future load of a system. Meanwhile, two filtering technologies from signal processing field are also introduced into this model to eliminate data noise and enhance prediction accuracy. The results of experiments conducted on a real grid environment demonstrate that this new model is more capable of predictingn-step-ahead load in a computational grid than previous works. The proposed hybrid model performs well on prediction advance time for up to 50 minutes, with significant less prediction errors than conventional AR model. It also achieves an interval length acceptable for task scheduler.