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Transfer learning based-hybrid model for short-term wind speed forecasting
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DOI：10.1016/j.egyr.2025.10.007.png)
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Abstract
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• Proposed a hybrid CEEMDAN-BiGRU-ED model for wind speed forecasting. • CEEMDAN decomposition minimizes noise and captures nonlinear patterns. • Bi-GRU Encoder-Decoder models temporal dependencies effectively. • Transfer learning boosts prediction accuracy and model adaptability. • The proposed model outperforms benchmarks across all evaluation metrics.
Keywords:
Wind speed forecasting
CEEMDAN
Bi-GRU Encoder-Decoder
Transfer learning
Artificial intelligence
Renewable energy
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