Using of improved models of Gaussian Processes in order to Regional wind power forecasting

Abstract The fluctuation of the wind speed and direction is due to the stochastic nature of the wind and the enforcing atmospheric pressure. Accordingly, the output power forecasting of the wind farms (WFs) will be difficult. In this paper, a novel method based on Gaussian Processes (GPs) is proposed to improve the probabilistic prediction of the WF levels and regional WFs. The Covariance Functions (CFs) are the key ingredient in using GPs. Thus, different groupings of CFs are investigated comprehensively. The GP includes different types as dynamic, static, direct, indirect, and combined structures, which are investigated in this study. The results of comparison between dynamic and static GP, reveal that the dynamic GP generates keen Prediction Intervals (PIs). In addition, with comparing the accuracy of direct and indirect prediction plan, it shows that indirect prediction strategy brings about wider PIs. The various evaluation metrics have applied to benchmark the different methods performance, and its results show that the indirect–dynamic GP has better performance than other combined structures of GP as well as other methods, in both WF levels and regional WFs, while its maximum error has obtained as about 5% less than others. Moreover, the proposed model provides precise results of forecasted energy in every time steps in both deterministic and probabilistic wind power forecasting. The compared results between indirect–dynamic GP and other structures show the highest average coverage error, about 1% and 2.2% higher in the regional level and WF levels, respectively, the lowest prediction interval nominalized average width, about 5% and 15% lower in the regional level and WF levels, respectively, and the highest interval sharpness, about 2% and 5% higher in the regional level and WF levels, respectively.