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Short-term wind power interval prediction method using VMD-RFG and Att-GRU

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  • Liu, Hongyi
  • Han, Hua
  • Sun, Yao
  • Shi, Guangze
  • Su, Mei
  • Liu, Zhangjie
  • Wang, Hongfei
  • Deng, Xiaofei

Abstract

With the increasing penetration of wind energy, accurate wind power prediction is essential for efficient utilization, equipment protection, and stable grid-connection of wind energy. Prediction interval (PI) provides a practical way to quantify the potential uncertainties of wind power. High coverage probability and narrow width are expected when constructing PI, however, the conflicts between these two objectives challenge the prediction accuracy. Therefore, a comprehensive PI quality evaluation strategy is presented in this paper to make a trade-off between coverage probability and interval width. It enables the prediction model to be trained by algorithms with high computational efficiency. Based on this evaluation strategy, a short-term wind power interval prediction method, which combines attention mechanism-based gated recurrent unit (Att-GRU), variational mode decomposition (VMD), and rolling fuzzy granulation (RFG), is proposed to construct high-quality PI and accurately capture the uncertainty of wind power. The validity of the proposed method is verified by comparative case studies on different datasets. Compared with other benchmark methods, the interval width is reduced by at least 22.66%, and the coverage probability and computation time have been promoted by at least 3.03% and 4.31% respectively.

Suggested Citation

  • Liu, Hongyi & Han, Hua & Sun, Yao & Shi, Guangze & Su, Mei & Liu, Zhangjie & Wang, Hongfei & Deng, Xiaofei, 2022. "Short-term wind power interval prediction method using VMD-RFG and Att-GRU," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007101
    DOI: 10.1016/j.energy.2022.123807
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    References listed on IDEAS

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    Cited by:

    1. Xinyue Fu & Zhongkai Feng & Xinru Yao & Wenjie Liu, 2023. "A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction," Energies, MDPI, vol. 16(15), pages 1-23, July.
    2. Hou, Guolian & Wang, Junjie & Fan, Yuzhen & Zhang, Jianhua & Huang, Congzhi, 2024. "A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation," Renewable Energy, Elsevier, vol. 226(C).
    3. Ai, Chunyu & He, Shan & Hu, Heng & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    4. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    5. Shi, Jinhao & Wang, Bo & Luo, Kaiyi & Wu, Yifei & Zhou, Min & Watada, Junzo, 2023. "Ultra-short-term wind power interval prediction based on multi-task learning and generative critic networks," Energy, Elsevier, vol. 272(C).
    6. Zhu, Qiannan & Jiang, Feng & Li, Chaoshun, 2023. "Time-varying interval prediction and decision-making for short-term wind power using convolutional gated recurrent unit and multi-objective elephant clan optimization," Energy, Elsevier, vol. 271(C).
    7. Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).

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