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An efficient short-term wind speed prediction model based on cross-channel data integration and attention mechanisms

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  • Yu, Enbo
  • Xu, Guoji
  • Han, Yan
  • Li, Yongle

Abstract

With the growing focus on renewable energy, wind power is increasingly valued and advocated. In order to guarantee the stability of wind power system dispatch and management, reliable prediction of future wind speeds is essential. In this study, a short-term wind speed prediction model based on cross-channel data convolution, intelligent signal extension and attention mechanisms is proposed to enhance the prediction efficiency. The model first classifies the wind speed signal into IMFs (intrinsic mode functions) and residual data with the EMD (empirical mode decomposition) method, and then divides IMFs into rough prediction part and accurate prediction part according to the signal characteristics. CNN (convolutional neural network) modules are adopted for the rough prediction part to ensure a speedy process, whereas a CNN-AM (attentional mechanism)-LSTM (long short-term memory)-ECA (efficient channel attention) hybrid network is developed to for the accurate prediction part. Through the time-history prediction on measured 10 min average wind speed data, the results show that: (a) The channel-crossing one-dimensional (1D) convolution, intelligent signal extension, and attention mechanisms applied in the proposed model can effectively improve the accuracy of predictions; (b) The proposed prediction model is superior to the compared baseline models in precision and efficiency; and (c) The proposed model features strong migration learning ability for fast application on new datasets.

Suggested Citation

  • Yu, Enbo & Xu, Guoji & Han, Yan & Li, Yongle, 2022. "An efficient short-term wind speed prediction model based on cross-channel data integration and attention mechanisms," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222014724
    DOI: 10.1016/j.energy.2022.124569
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Yi-Ming & Wang, Hao, 2023. "Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting," Energy, Elsevier, vol. 278(PA).
    2. Jingming Su & Xuguang Han & Yan Hong, 2023. "Short Term Power Load Forecasting Based on PSVMD-CGA Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    3. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Liu, Zhijiang & Ke, Yun & Ding, Shunliang, 2023. "Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control," Energy, Elsevier, vol. 274(C).
    4. Yan Hong & Ding Wang & Jingming Su & Maowei Ren & Wanqiu Xu & Yuhao Wei & Zhen Yang, 2023. "Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model," Sustainability, MDPI, vol. 15(14), pages 1-28, July.

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