基于ICPO优化VMD耦合深度学习模型的中短期风电功率预测

黄伟; 刘彬; 李火坤; 黄俊; 黄梓阳

doi:10.19912/j.0254-0096.tynxb.2024-1820

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (2) : 546-557. DOI: 10.19912/j.0254-0096.tynxb.2024-1820

基于ICPO优化VMD耦合深度学习模型的中短期风电功率预测

黄伟¹, 刘彬¹, 李火坤¹, 黄俊², 黄梓阳¹

作者信息 +

MEDIUM-SHORT TERM WIND POWER FORECASTING BASED ON ICPO OPTIMIZATION VMD COUPLED DEEP LEARNING MODEL

Huang Wei¹, Liu Bin¹, Li Huokun¹, Huang Jun², Huang Ziyang¹

Author information +

文章历史 +

摘要

为提高风电功率的预测精度,增强混合模型的泛化性能,提出一种基于变分模态分解（VMD）耦合双向时域卷积网络（BiTCN）、双向长短期记忆网络（BiLSTM）和注意力机制（Attention）的混合中短期风电预测模型,并利用改进的冠豪猪算法（ICPO）优化VMD分解参数以及混合模型参数。该方法首先利用ICPO对VMD核心参数（K值和惩罚系数α）寻优,将原有的风电功率序列进行VMD分解;再引入ICPO对BiTCN-BiLSTM-Attention深度学习模型的超参数进行自动寻优,针对分解后的各分量分别建立ICPO-BiTCN-BiLSTM-Attention预测模型;最后叠加各分量的预测值得到最终预测值。某风电场实例验证表明,相比于单一预测模型和常规组合模型,提出的耦合模型在功率预测精度与泛化性能上均实现了显著提升。

Abstract

To improve the forecast accuracy of wind power and the generalization performance of the hybrid model, this paper proposes a hybrid medium-short-term wind power forecast model based on variational mode decomposition （VMD） coupled with bidirectional time convolutional network （BiTCN）, bidirectional long short-term memory network （BiLSTM）, and Attention mechanism. Furthermore, the model uses the improved crested porcupine optimizer （ICPO） to optimize the VMD decomposition parameters and the parameters of the hybrid model. Firstly, the ICPO is used to optimize the core VMD parameters （K value and penalty coefficient α）, and the original wind power sequence is decomposed by VMD. Then, the ICPO is introduced to automatically optimize the hyperparameters of the BiTCN-BiLSTM-Attention deep learning model, and the ICPO-BiTCN-BiLSTM-Attention forecast model is established for each component after decomposition. Finally, the prediction values of each component are superimposed to obtain the final prediction value. The verification of a certain wind farm instance indicates that compared with the single forecast models and the conventional combination models, the coupling model proposed in this paper achieves remarkable enhancements in both prediction accuracy and generalization performance.

导出引用

黄伟, 刘彬, 李火坤, 黄俊, 黄梓阳. 基于ICPO优化VMD耦合深度学习模型的中短期风电功率预测[J]. 太阳能学报. 2026, 47(2): 546-557 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1820

Huang Wei, Liu Bin, Li Huokun, Huang Jun, Huang Ziyang. MEDIUM-SHORT TERM WIND POWER FORECASTING BASED ON ICPO OPTIMIZATION VMD COUPLED DEEP LEARNING MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 546-557 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1820

中图分类号： TK8

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