WIND POWER PREDICTION BASED ON MODWT-BiLSTM-BiGRU

Guo Lijin; Liu Wenzhe; Liu Yanbin

doi:10.19912/j.0254-0096.tynxb.2024-2256

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 405-413. DOI: 10.19912/j.0254-0096.tynxb.2024-2256

WIND POWER PREDICTION BASED ON MODWT-BiLSTM-BiGRU

Guo Lijin^1,2, Liu Wenzhe^1,2, Liu Yanbin^1,2

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Abstract

To enhance the accuracy of wind power predictions, a novel model is proposed in this paper that integrates the maximum overlap discrete wavelet transform （MODWT） with a bidirectional recurrent neural network. The MODWT is utilized to decompose wind power into its low-frequency and high-frequency components. In conjunction, the bidirectional long short-term memory （BiLSTM） and bidirectional gated recurrent unit （BiGRU） work synergistically to effectively predict both short-term and long-term wind power data, leading to significantly accurate predictions. Experiments were conducted using the actual data of two wind farms. The results confirm that the proposed method has consistent superiority over the baseline method. A substantial improvement in prediction accuracy is obtained. The error metrics including root mean square error （RMSE） and mean absolute error （MAE） are reduced by approximately 16.47% and 15.10%, respectively, while the coefficient of determination （R²） achieved a value of 0.9756.

Key words

wind power forecasting / long short-term memory network / gated recirculation unit / non-stationarity / discrete wavelet transform / prediction model

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Guo Lijin, Liu Wenzhe, Liu Yanbin. WIND POWER PREDICTION BASED ON MODWT-BiLSTM-BiGRU[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 405-413 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2256

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