Wind power prediction using stacking and transfer learning

doi:10.1038/s41598-025-96262-6

Wind power prediction using stacking and transfer learning

2025-04-04

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DOI：10.1038/s41598-025-96262-6

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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.

Keyword:

Long short-term memory

Principal component analysis

Stacking ensemble model

Transfer learning

Ultra-short-term prediction

Wind power

Wind power prediction using stacking and transfer learning

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