电力系统保护与控制2025，Vol.53Issue(19)：68-78,11.DOI:10.19783/j.cnki.pspc.241547
增强的超短期风电功率预测:一种PatchTST-POA-VMD-iTransformer混合模型
Enhanced ultra-short-term wind power forecasting:a PatchTST-POA-VMD-iTransformer hybrid model
摘要
Abstract
Due to the high sensitivity of wind power generation to weather changes,the output power of wind farms fluctuates significantly over time.Traditional prediction models struggle to accurately forecast wind farm output power and effectively correct wind power forecasting errors.To address these issues,a PatchTST-POA-VMD-iTransformer hybrid prediction model is proposed.First,the Spearman's rank correlation coefficient method is employed for quantifying the correlation between weather features and wind power,enabling data screening and preprocessing.Then,PatchTST is introduced for preliminary prediction of wind farm output power,yielding initial power forecasting results.Subsequently,pelican optimization algorithm(POA)optimized variational mode decomposition(VMD)is used to decompose wind power forecasting error sequence,and iTransformer is applied to further predict the decomposed error sequence.Finally,the preliminary power forecasting results are combined with the predicted error sequence to obtain the final wind power forecasting results.Ablation and comparative experiment results demonstrate that the proposed model achieves lower prediction errors and superior generalization ability,effectively improving the accuracy and reliability of ultra-short-term wind power forecasting.关键词
风电功率预测/PatchTST/鹈鹕优化算法/变模态分解/iTransformerKey words
wind power forecasting/PatchTST/POA/VMD/iTransformer引用本文复制引用
刘新宇,潘宇,王亚辉,李继方,杨文静..增强的超短期风电功率预测:一种PatchTST-POA-VMD-iTransformer混合模型[J].电力系统保护与控制,2025,53(19):68-78,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.U1804149). 国家自然科学基金项目资助(U1804149) （No.U1804149）
河南省高等学校重点科研项目资助(25B120003) （25B120003）
河南省科技攻关项目资助(252102210038) （252102210038）
