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<p>Wind power generation is highly sensitive to meteorological conditions, leading to strong volatility and limited controllability. Accurate short-term wind power forecasting is therefore critical for maintaining power system stability and improving the utilization of renewable energy. In this work, we propose an enhanced forecasting model termed Particle Swarm Optimization Residual Structure Convolutional Neural Network Long Short Term Memory (PR-CNN-LSTM), which extends the conventional CNN-LSTM architecture. The model first employs CNN-LSTM to capture local patterns and temporal dependencies across different forecasting horizons. Residual structures are then introduced into both the convolutional and recurrent modules to stabilize deep network training and enhance representation learning. Furthermore, particle swarm optimization is incorporated to dynamically tune key model hyperparameters, enabling adaptive selection of an optimal parameter configuration. Extensive experiments demonstrate that PR-CNN-LSTM consistently outperforms representative baseline models under all evaluated forecasting scenarios. In single-step forecasting, the proposed model achieves an <i>R</i><sup>2</sup> exceeding 0.97, indicating strong accuracy and robustness. These results suggest that the proposed approach provides effective technical support for smart grid scheduling, power balance management, and the large-scale integration of wind energy.</p>

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Short-term wind power forecasting based on an improved CNN-LSTM model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月29日

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  • Xinyue Hu,
  • Mingda Guo,
  • Haoming Lan

摘要

Wind power generation is highly sensitive to meteorological conditions, leading to strong volatility and limited controllability. Accurate short-term wind power forecasting is therefore critical for maintaining power system stability and improving the utilization of renewable energy. In this work, we propose an enhanced forecasting model termed Particle Swarm Optimization Residual Structure Convolutional Neural Network Long Short Term Memory (PR-CNN-LSTM), which extends the conventional CNN-LSTM architecture. The model first employs CNN-LSTM to capture local patterns and temporal dependencies across different forecasting horizons. Residual structures are then introduced into both the convolutional and recurrent modules to stabilize deep network training and enhance representation learning. Furthermore, particle swarm optimization is incorporated to dynamically tune key model hyperparameters, enabling adaptive selection of an optimal parameter configuration. Extensive experiments demonstrate that PR-CNN-LSTM consistently outperforms representative baseline models under all evaluated forecasting scenarios. In single-step forecasting, the proposed model achieves an R2 exceeding 0.97, indicating strong accuracy and robustness. These results suggest that the proposed approach provides effective technical support for smart grid scheduling, power balance management, and the large-scale integration of wind energy.