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A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization

Author

Listed:
  • Meng, Anbo
  • Chen, Shun
  • Ou, Zuhong
  • Ding, Weifeng
  • Zhou, Huaming
  • Fan, Jingmin
  • Yin, Hao

Abstract

Accurate wind power forecasting is of great significance for power system operation. In this study, a triple-stage multi-step wind power forecasting approach is proposed by applying attention-based deep residual gated recurrent unit (GRU) network combined with ensemble empirical mode decomposition (EEMD) and crisscross optimization algorithm (CSO). In the data processing stage, the EEMD is used to decompose the wind power/speed time series and a bi-attention mechanism (BA) is applied to enhance the sensitivity of model to the important information from both time and feature dimension. In the prediction stage, a series-connected deep learning model called RGRU consisting of the residual network and GRU is proposed as the forecasting model, aiming to make full use of extracting the static and dynamic coupling relationship among the input features. In the retraining-stage, a high-performance CSO algorithm is adopted to further optimize the fully-connected layer of RGRU model so as to improve the generalization ability of the model. The proposed method is validated on a wind farm located in Spain and the experimental results demonstrate that the proposed hybrid model has significant advantage over other state-of-the-art models involved in this study in terms of prediction accuracy and stability.

Suggested Citation

  • Meng, Anbo & Chen, Shun & Ou, Zuhong & Ding, Weifeng & Zhou, Huaming & Fan, Jingmin & Yin, Hao, 2022. "A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization," Energy, Elsevier, vol. 238(PB).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020430
    DOI: 10.1016/j.energy.2021.121795
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    Cited by:

    1. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    2. Meng, Anbo & Chen, Shu & Ou, Zuhong & Xiao, Jianhua & Zhang, Jianfeng & Chen, Shun & Zhang, Zheng & Liang, Ruduo & Zhang, Zhan & Xian, Zikang & Wang, Chenen & Yin, Hao & Yan, Baiping, 2022. "A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network," Energy, Elsevier, vol. 261(PA).
    3. Renxi Gong & Xianglong Li, 2023. "A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism," Energies, MDPI, vol. 16(6), pages 1-24, March.
    4. 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).
    5. Chen, Wenhe & Zhou, Hanting & Cheng, Longsheng & Xia, Min, 2023. "Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention," Energy, Elsevier, vol. 278(PB).
    6. Wu, Qiang & Zheng, Hongling & Guo, Xiaozhu & Liu, Guangqiang, 2022. "Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks," Renewable Energy, Elsevier, vol. 199(C), pages 977-992.
    7. Guo, Nai-Zhi & Shi, Ke-Zhong & Li, Bo & Qi, Liang-Wen & Wu, Hong-Hui & Zhang, Zi-Liang & Xu, Jian-Zhong, 2022. "A physics-inspired neural network model for short-term wind power prediction considering wake effects," Energy, Elsevier, vol. 261(PA).

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