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Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model

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  • Haotian Guo

    (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China
    Department of Electrical and Computer Engineering, University of Macau, Macao 999078, China)

  • Keng-Weng Lao

    (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China
    Department of Electrical and Computer Engineering, University of Macau, Macao 999078, China)

  • Junkun Hao

    (School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China)

  • Xiaorui Hu

    (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China
    Department of Electrical and Computer Engineering, University of Macau, Macao 999078, China)

Abstract

Driven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three key challenges in extracting temporal characteristics of strong volatility, adaptive fusion of multi-source features, and enhancing model interpretability, this paper proposes a Time-Domain Dual-Channel Adaptive Learning Model (TDDCALM). The model employs dual-channel feature decoupling: one Transformer encoder layer captures global dependencies while the raw state layer preserves local temporal features. After TCN-based feature compression, an adaptive weighted early fusion mechanism dynamically optimizes channel weights. The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. Validation on two wind farm datasets (A/B) demonstrates that the proposed method reduces RMSE by at least 8.89% compared to the best deep learning baseline, exhibits low sensitivity to time window sizes, and establishes a novel paradigm for forecasting highly volatile renewable energy power.

Suggested Citation

  • Haotian Guo & Keng-Weng Lao & Junkun Hao & Xiaorui Hu, 2025. "Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model," Energies, MDPI, vol. 18(14), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3722-:d:1701351
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    References listed on IDEAS

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    1. Zhang, Yagang & Kong, Xue & Wang, Jingchao & Wang, Hui & Cheng, Xiaodan, 2024. "Wind power forecasting system with data enhancement and algorithm improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    2. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
    3. Cai, Yizhuo & Li, Yanting, 2024. "Short-term wind speed forecast based on dynamic spatio-temporal directed graph attention network," Applied Energy, Elsevier, vol. 375(C).
    4. Liu, Lei & Wang, Xinyu & Dong, Xue & Chen, Kang & Chen, Qiuju & Li, Bin, 2024. "Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series," Applied Energy, Elsevier, vol. 374(C).
    5. Chen, Fuhao & Yan, Jie & Liu, Yongqian & Yan, Yamin & Tjernberg, Lina Bertling, 2024. "A novel meta-learning approach for few-shot short-term wind power forecasting," Applied Energy, Elsevier, vol. 362(C).
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