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<p>Accurate short-term wind power prediction (WPP) is critical for power system stability but remains challenging due to the inherent non-linearity and volatility of wind series. This study proposes a novel framework, SVMD-RIME-TCN-BiGRU, to address these challenges. First, the Maximal Information Coefficient (MIC) is used to select high-correlation features and eliminate redundancy. Second, Successive Variational Mode Decomposition (SVMD) decomposes raw data into successive intrinsic modes, effectively mitigating non-stationarity and avoiding the mode-mixing issues of traditional methods. Third, a hybrid Temporal Convolutional Network-Bidirectional Gated Recurrent Unit (TCN-BiGRU) model is constructed to extract spatiotemporal features. Crucially, the RIME optimization algorithm is introduced to automatically tune the key hyperparameters of the TCN-BiGRU, avoiding local optima. Experimental results on a Xinjiang wind farm dataset demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.6882 and an R² of 0.9813. It significantly outperforms baseline models (including LSTM, TCN, and Transformer) and other hybrid variants, reducing errors by over 38% compared to the TCN-BiGRU baseline. This validates the framework’s reliability and accuracy for practical power dispatching.</p>

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A hybrid SVMD-RIME-TCN-BiGRU model for wind power prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月19日

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  • Kaikai Gu,
  • Lei Cao,
  • Jing Cao,
  • Mu LI,
  • Hanchao Chen,
  • Zhong Wang,
  • Sheng Liu,
  • Kefei Zhang

摘要

Accurate short-term wind power prediction (WPP) is critical for power system stability but remains challenging due to the inherent non-linearity and volatility of wind series. This study proposes a novel framework, SVMD-RIME-TCN-BiGRU, to address these challenges. First, the Maximal Information Coefficient (MIC) is used to select high-correlation features and eliminate redundancy. Second, Successive Variational Mode Decomposition (SVMD) decomposes raw data into successive intrinsic modes, effectively mitigating non-stationarity and avoiding the mode-mixing issues of traditional methods. Third, a hybrid Temporal Convolutional Network-Bidirectional Gated Recurrent Unit (TCN-BiGRU) model is constructed to extract spatiotemporal features. Crucially, the RIME optimization algorithm is introduced to automatically tune the key hyperparameters of the TCN-BiGRU, avoiding local optima. Experimental results on a Xinjiang wind farm dataset demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.6882 and an R² of 0.9813. It significantly outperforms baseline models (including LSTM, TCN, and Transformer) and other hybrid variants, reducing errors by over 38% compared to the TCN-BiGRU baseline. This validates the framework’s reliability and accuracy for practical power dispatching.