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<p>Wind power generation is a vital component of renewable energy, and achieving high-accuracy power forecasting is crucial for grid stability and sustainable operation. To address the highly nonlinear and complex temporal characteristics of wind power, this paper investigates a hybrid deep learning model, TCN–SENet–BiGRU–Global Attention. The model integrates a Temporal Convolutional Network (TCN), Squeeze-and-Excitation Network (SENet), Bidirectional Gated Recurrent Unit (BiGRU), and a Global Attention mechanism to construct a multi-level feature extraction architecture. Specifically, TCN efficiently captures both long-term and short-term temporal dependencies, SENet enhances the impact of key variables by adaptively adjusting channel-wise feature weights, BiGRU models bidirectional temporal context, and the Global Attention mechanism focuses on informative time steps to better track dynamic changes in wind power. Experiments on multiple real-world datasets from a wind farm demonstrate that the proposed TCN–SENet–BiGRU–Global Attention model achieves consistently lower prediction errors and more stable performance than several representative baseline models, indicating its good robustness and promising application potential for complex short-term wind power forecasting tasks.</p>

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Research on enhancing short-term wind power forecasting through feature fusion in a hybrid deep learning framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月20日

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  • Xianlong Su,
  • Jinming Gao,
  • Kai Han,
  • Hankil Kim,
  • Hoekyung Jung

摘要

Wind power generation is a vital component of renewable energy, and achieving high-accuracy power forecasting is crucial for grid stability and sustainable operation. To address the highly nonlinear and complex temporal characteristics of wind power, this paper investigates a hybrid deep learning model, TCN–SENet–BiGRU–Global Attention. The model integrates a Temporal Convolutional Network (TCN), Squeeze-and-Excitation Network (SENet), Bidirectional Gated Recurrent Unit (BiGRU), and a Global Attention mechanism to construct a multi-level feature extraction architecture. Specifically, TCN efficiently captures both long-term and short-term temporal dependencies, SENet enhances the impact of key variables by adaptively adjusting channel-wise feature weights, BiGRU models bidirectional temporal context, and the Global Attention mechanism focuses on informative time steps to better track dynamic changes in wind power. Experiments on multiple real-world datasets from a wind farm demonstrate that the proposed TCN–SENet–BiGRU–Global Attention model achieves consistently lower prediction errors and more stable performance than several representative baseline models, indicating its good robustness and promising application potential for complex short-term wind power forecasting tasks.