Ensemble wind speed prediction system based on envelope decomposition method and fuzzy inference evaluation of predictability

As an indispensable part of the current global power system, wind energy has always been the focus of research over the world. In the production process of wind power, wind speed is a crucial factor and the requirements for wind speed prediction accuracy are increasing in practical applications. Therefore, as the main contribution of this paper, a novel decomposition-recognition-integration-prediction system (DRIPS) is proposed based on a newly developed predictive difficulty index (PDI) that synthesizes complexity, chaos, and long-term dependence of time series data. PDI comprehensively quantifies the basic characteristics and prediction difficulty of the series, filling the gap in the existing intuitionistic evaluation method. To verify the predictive ability and effectiveness of DRIPS, we select two American on-shore wind sites (Nolan and Kern) as the site of the experiments. At each site, the 10-minuteinterval wind speed for three months in 2018 is collected as experimental samples. The simulation results show that DRIPS can provide excellent performance in the accuracy of wind speed prediction. In terms of deterministic prediction and uncertainty prediction, DRIPS performs less than 2% on the mean absolute percentage error for point prediction and less than 0.5 on the predictive interval score for interval prediction. Such performance is significantly better than that of the common prediction models such as BP, SVM, etc. Moreover, By comparing the experimental results of different integration strategies, the integration strategy based on PDI can improve the prediction accuracy significantly. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This article proposes a novel wind speed prediction system (DRIPS) that improves the accuracy of wind speed prediction by integrating complexity, chaos, and long-term dependence indicators (PDI). Experimental results show that DRIPS performs significantly better than common prediction models and that the integration strategy based on PDI significantly improves prediction accuracy.