| 引用本文: | 刘新宇,潘 宇,王亚辉,等.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.[点击复制] |
| LIU Xinyu,PAN Yu,WANG Yahui,et al.Enhanced ultra-short-term wind power forecasting: a PatchTST-POA-VMD-iTransformer hybrid model[J].Power System Protection and Control,2025,53(19):68-78[点击复制] |
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| 摘要: |
| 由于风力发电对天气变化的敏感度高，风电场输出功率随时间变化的波动大，传统预测模型难以对风电场输出功率进行准确预测，也难以对风电预测误差进行有效修正。针对上述问题，提出了一种PatchTST-POA-VMD- iTransformer混合预测模型。首先，基于斯皮尔曼相关系数法进行天气特征与风电功率相关性量化分析，完成数据筛选和预处理。然后，引入PatchTST对风电场输出功率进行初步预测，得到初步预测的功率结果。随后，采用鹈鹕优化算法(pelican optimization algorithm, POA)优化的变模态分解(variational mode decomposition, VMD)对风电预测误差序列进行分解，再使用iTransformer对分解后的误差序列进行预测。最后，将已获得的初步功率预测结果与误差序列预测结果相结合，得到最终的风电功率预测结果。消融实验和对比实验结果表明，所提模型具有较小的预测误差和较优的泛化能力，能够有效提升超短期风电功率预测的精度和可靠性。 |
| 关键词: 风电功率预测 PatchTST 鹈鹕优化算法 变模态分解 iTransformer |
| DOI：10.19783/j.cnki.pspc.241547 |
| 投稿时间：2024-11-21修订日期：2025-04-07 |
| 基金项目:国家自然科学基金项目资助(U1804149)；河南省高等学校重点科研项目资助(25B120003)；河南省科技攻关项目资助(252102210038) |
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| Enhanced ultra-short-term wind power forecasting: a PatchTST-POA-VMD-iTransformer hybrid model |
| LIU Xinyu,PAN Yu,WANG Yahui,LI Jifang,YANG Wenjing |
| (College of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China) |
| 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. |
| Key words: wind power forecasting PatchTST POA VMD iTransformer |
