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<p>As countries focus more on renewable energy, especially wind power, predicting wind power output accurately is crucial for managing power grids and saving costs. This paper presents a new method for ultra-short-term wind power prediction using a combination of Stacking and Transfer Learning. To improve accuracy, we first reduce the data dimensions using PCA. Then, we use several models like LSTM, BiLSTM, GRU, BiGRU, and LSTM-Attention as base learners. These models are combined using a Stacking ensemble model. We also use Transfer Learning to share trained models between tasks, which helps improve performance. Tests with real data from a wind farm show that our method is more accurate than single models.</p>

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Wind power prediction using stacking and transfer learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月4日

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  • Xu Cheng,
  • Yu Cao,
  • Zhiyuan Song,
  • Chenguang Zhang

摘要

As countries focus more on renewable energy, especially wind power, predicting wind power output accurately is crucial for managing power grids and saving costs. This paper presents a new method for ultra-short-term wind power prediction using a combination of Stacking and Transfer Learning. To improve accuracy, we first reduce the data dimensions using PCA. Then, we use several models like LSTM, BiLSTM, GRU, BiGRU, and LSTM-Attention as base learners. These models are combined using a Stacking ensemble model. We also use Transfer Learning to share trained models between tasks, which helps improve performance. Tests with real data from a wind farm show that our method is more accurate than single models.