Very Short-term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIsOA
Due to the uncertainty and fluctuation of wind power generation,probabilistic prediction for regional wind power generation is critical to accurately quantify the uncertainty of meaningful information to the dispatching departments of power grid.This paper proposes an approach of very short-term probabilistic prediction for regional wind power generation based on optimal performance-based nonparametric prediction intervals(OPNPIs).First,the deterministic prediction for regional wind power generation considering the division of wind farms based on the detrending-based partial cross-correlation analysis(DPCCA)is studied.Based on the deterministic prediction and its prediction errors,the OPNPIs are proposed considering the reliability and overall performance for the uncertainty analysis.Furthermore,a regulating coefficient is studied to further enhance the performance of PIs.Effectiveness of the proposed method is verified through multistep PIs of 15-minute based on the real wind power generation data.
Yan Zhou;Yonghui Sun;Sen Wang;Rabea Jamil Mahfoud;Dongchen Hou;Jianxi Wang
College of Energy and Electrical Engineering,Hohai University,Nanjing 210098,ChinaCollege of Energy and Electrical Engineering,Hohai University,Nanjing 210098,ChinaCollege of Energy and Electrical Engineering,Hohai University,Nanjing 210098,ChinaCollege of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,ChinaCollege of Energy and Electrical Engineering,Hohai University,Nanjing 210098,ChinaCollege of Energy and Electrical Engineering,Hohai University,Nanjing 210098,China
信息技术与安全科学
Detrending-basedpartialcross-correlationanalysisHuber-basedapproachnonparametricpredictionintervalsoverallperformanceregionalwindpowergeneration
《CSEE Journal of Power and Energy Systems》 2026 (2)
P.803-812,10
supported by the National Natural Science Foundation of China(62073121)National Key R&D Program of China Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption(2018YFB0904200)eponymous Complement S&T Program of State Grid Corporation of China(SGLNDK0OKJJS1800266)。
评论