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A short-term wind power prediction method based on dynamic and static feature fusion mining

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  • Yang, Mao
  • Wang, Da
  • Zhang, Wei

Abstract

Wind power is a kind of time-varying time series with fluctuation characteristics. To take full advantage of the time-varying value provided by wind power fluctuations, a short-term wind power prediction method based on dynamic and static feature fusion mining is proposed. First, three statistical features are manually constructed to characterize the dynamic fluctuation of wind speed, these features provide more valuable patterns for the input data. Then, we construct a residual network structure that incorporates the bidirectional gate recurrent unit, and incorporate temporal and spatial attention mechanisms in the network structure. This network structure is used to train the wind power prediction model, which has great advantages in reducing the degradation and overfitting problems caused by increasing the depth of the network. Finally, a wind power prediction index is proposed to quantify the proportion of NWP link error and modeling link error in the total error. Simulation experiments were conducted on a wind farm with an installed capacity of 400.5 MW in Jilin Province, China, and the predicted NRMSE is 0.1581.

Suggested Citation

  • Yang, Mao & Wang, Da & Zhang, Wei, 2023. "A short-term wind power prediction method based on dynamic and static feature fusion mining," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223016201
    DOI: 10.1016/j.energy.2023.128226
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    Cited by:

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    2. Liu, Zhi-Feng & Liu, You-Yuan & Chen, Xiao-Rui & Zhang, Shu-Rui & Luo, Xing-Fu & Li, Ling-Ling & Yang, Yi-Zhou & You, Guo-Dong, 2024. "A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting," Applied Energy, Elsevier, vol. 360(C).
    3. Yang, Mao & Guo, Yunfeng & Huang, Yutong, 2023. "Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process," Energy, Elsevier, vol. 282(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. Yang, Mao & Han, Chao & Zhang, Wei & Wang, Bo, 2024. "A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information," Energy, Elsevier, vol. 294(C).
    6. Yang, Mao & Huang, Yutong & Guo, Yunfeng & Zhang, Wei & Wang, Bo, 2024. "Ultra-short-term wind farm cluster power prediction based on FC-GCN and trend-aware switching mechanism," Energy, Elsevier, vol. 290(C).

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