转折性天气下日前风电功率区间预测的鲁棒性误差修正策略OA

[Objectives]In recent years,transitional weather has occurred frequently,and the randomness and volatility of wind power generation have intensified.Existing day-ahead wind power prediction schemes struggle to balance the interval coverage rate and interval width of wind power prediction.Therefore,a robust error correction strategy is proposed to achieve high-quality day-ahead wind power interval prediction.[Methods]First,a kernel-based fuzzy C-means clustering algorithm is proposed,which is combined with multi-step backward cloud transformation based on sampling with replacement to accurately cluster error types under complex transitional weather conditions.Then,a point prediction model based on temporal convolutional network-Transformer is established,and an improved kernel density estimation day-ahead wind power interval prediction method is designed to improve the fitting accuracy of power interval.Finally,an improved multi-objective dung beetle optimizer algorithm is designed to perform robust error correction across different clusters,and the prediction performance of the proposed method is verified using measured wind power data.[Results]The day-ahead wind power prediction interval after robust error correction exhibits a smaller interval width and higher interval coverage rate.Under the 95%confidence level,prediction interval coverage probability increases by a maximum of 5.884%,and prediction interval normalized average width reduces by a maximum of 35.01%,validating the effectiveness of the proposed method.[Conclusions]The proposed method significantly improves the fitting accuracy of error distribution,avoids the problem of poor interval quality caused by local over-correction or under-correction,and greatly enhances the efficiency of the algorithm search,thereby achieving higher-quality wind power interval prediction.