SHORT TERM WIND POWER PREDICTION USING CNN-GRU-LightGBM MODEL BASED ON EMPEROR BUTTERFLY ALGORITHM

Xiang Yang; Liu Yajuan; Sun Zhiwei; Zhang Xiaoning; Lu Jianmou

doi:10.19912/j.0254-0096.tynxb.2023-1388

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 105-114. DOI: 10.19912/j.0254-0096.tynxb.2023-1388

SHORT TERM WIND POWER PREDICTION USING CNN-GRU-LightGBM MODEL BASED ON EMPEROR BUTTERFLY ALGORITHM

Xiang Yang¹, Liu Yajuan¹, Sun Zhiwei¹, Zhang Xiaoning², Lu Jianmou¹

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Abstract

Uncertainties arising from large-scale grid integration and cross-seasonal use of wind power clusters place higher demands on the accuracy of wind power prediction. To improve the accuracy of wind power prediction, a Convolutional Neural Networks （CNN） Gated Recurrent Unit （GRU） -LightGBM Gradient Boosting Machine （LightGBM） composite wind power prediction model based on Monarch Butterfly Optimization （MBO） algorithm is proposed. Firstly, establish wind power prediction models for CNN-GRU and LightGBM respectively, and use the reciprocal variance method to weight and combine the two models into a CNN-GRU LightGBM composite model; To optimize the continuous parameters in the model, MBO is used to perform hyperparameter optimization on the model. Finally, short-term wind power data from an offshore wind farm in Zhuhai was selected to compare the proposed method with existing prediction methods. The experimental results showed that the model combined the advantages of models such as CNN-GRU and LightGBM, resulting in smaller prediction errors, higher prediction accuracy, and stronger seasonal universality.

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wind power forecast / convolutional neural networks / gated circulation unit / gradient boosting learning / monarch butterfly algorithm

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Xiang Yang, Liu Yajuan, Sun Zhiwei, Zhang Xiaoning, Lu Jianmou. SHORT TERM WIND POWER PREDICTION USING CNN-GRU-LightGBM MODEL BASED ON EMPEROR BUTTERFLY ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 105-114 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1388

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