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<p>The volatility and non-stationarity of wind speed can present a substantial threat to wind power systems. &#xa0;In extreme circumstances, it may even cause grid collapse. Hence, guaranteeing the accuracy and dependability of wind speed time series prediction holds significant importance for maintaining the safe and stable operation of wind farms. Thus, a wind speed forecasting model with high accuracy and stability is deliberated in this paper. The model encompasses a data preprocessing module, an optimization module, and an ensemble learning prediction module. In the data preprocessing module, the approaches of isolated forest and Kalman filter are employed to detect outliers and reduce noise. In the optimization module, the meta-heuristic algorithm is utilized to optimize the hyperparameters. In the ensemble learning prediction module, the prediction outcomes are obtained by integrating the results of multiple models. The outcomes demonstrate that the system possesses reliable and high-precision prediction performance. The research findings have crucial practical significance for enhancing the operational efficiency of wind power generation, strengthening the supply and demand balance of the power grid, and augmenting the economic and environmental advantages of wind power generation.</p>

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Wind speed time series forecasting system based on multi-model ensemble learning technology

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • Runze Li,
  • Jianzhou Wang,
  • Yamei Chen,
  • Jingrui Li

摘要

The volatility and non-stationarity of wind speed can present a substantial threat to wind power systems.  In extreme circumstances, it may even cause grid collapse. Hence, guaranteeing the accuracy and dependability of wind speed time series prediction holds significant importance for maintaining the safe and stable operation of wind farms. Thus, a wind speed forecasting model with high accuracy and stability is deliberated in this paper. The model encompasses a data preprocessing module, an optimization module, and an ensemble learning prediction module. In the data preprocessing module, the approaches of isolated forest and Kalman filter are employed to detect outliers and reduce noise. In the optimization module, the meta-heuristic algorithm is utilized to optimize the hyperparameters. In the ensemble learning prediction module, the prediction outcomes are obtained by integrating the results of multiple models. The outcomes demonstrate that the system possesses reliable and high-precision prediction performance. The research findings have crucial practical significance for enhancing the operational efficiency of wind power generation, strengthening the supply and demand balance of the power grid, and augmenting the economic and environmental advantages of wind power generation.