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

Huang Wei; Liu Bin; Li Huokun; Huang Jun; Huang Ziyang

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

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 546-557. DOI: 10.19912/j.0254-0096.tynxb.2024-1820

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

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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.

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wind power / forecasting / deep learning / adaptive algorithms / variational mode decomposition

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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

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