ONLINE WIND POWER FORECASTING METHOD BASED ON DYNAMIC DEEP LEARNING

Zhao Hongshan; Yang Duo; Liu Xinyu; Ni Hengyi; Zhang Yangfan; Lin Shiyu

doi:10.19912/j.0254-0096.tynxb.2024-0885

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 171-180. DOI: 10.19912/j.0254-0096.tynxb.2024-0885

ONLINE WIND POWER FORECASTING METHOD BASED ON DYNAMIC DEEP LEARNING

Zhao Hongshan¹, Yang Duo¹, Liu Xinyu¹, Ni Hengyi¹, Zhang Yangfan², Lin Shiyu¹

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Abstract

Accurate wind power forecasting is crucial for the reliability of grid scheduling decisions. To address the variability in wind power output, a wind power online forecasting method based on dynamic deep learning is proposed. Initially, a benchmark model utilizing bidirectional Long Short-Term Memory（LSTM） networks and bidirectional Gated Recurrent Units（GRU） is developed, with parameters and weights tailored to the training dataset. Subsequently, a fast Hoeffding drift detection method is used for wind power state monitoring, and the deep learning model is dynamically updated based on the detection results. Finally, a Random Forest regression model is integrated to correct the predicted power errors, enabling rolling online forecasting through sequential time windows. Validation results indicate that the proposed algorithm improves the root mean square error（RMSE） by 5.68%, the mean absolute error（MAE） by 18.56%, and the coefficient of determination（R²） by 2.06% compared to the Transformer, demonstrating good predictive performance and further enhancing the accuracy of wind power forecasting.

Key words

wind power forecasting / dynamic deep learning / online forecasting / BiLSTM / BiGRU / random forest

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Zhao Hongshan, Yang Duo, Liu Xinyu, Ni Hengyi, Zhang Yangfan, Lin Shiyu. ONLINE WIND POWER FORECASTING METHOD BASED ON DYNAMIC DEEP LEARNING[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 171-180 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0885

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