发电技术2026，Vol.47Issue(3)：665-676,12.DOI:10.12096/j.2096-4528.pgt.260320
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
A Robust Error Correction Strategy for Day-Ahead Wind Power Interval Prediction in Transitional Weather
摘要
Abstract
[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.关键词
转折性天气/日前风功率预测/逆向云变换/误差类型聚类/修正权重优化/核密度估计Key words
transitional weather/day-ahead wind power prediction/backward cloud transformation/error type clustering/correction weight optimization/kernel density estimation分类
能源科技引用本文复制引用
丁贵立,郭洋,舒展,颜高洋,崔明建,辛建波,王华云,钟智强,周世阳,韩信..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[J].发电技术,2026,47(3):665-676,12.基金项目
国家自然科学基金项目(52207130).Project Supported by National Natural Science Foundation of China(52207130). （52207130）
