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A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects

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  • Qiu, Hong
  • Shi, Kaikai
  • Wang, Renfang
  • Zhang, Liang
  • Liu, Xiufeng
  • Cheng, Xu

Abstract

Wind Power Forecasting is crucial for the operational security, stability, and economic efficiency of the power grid, yet it faces significant accuracy challenges due to the variable nature of wind energy and complex interactions within wind farms. This study introduces a novel neural network model specifically designed for Wind Power Forecasting, incorporating both a gated dilated inception network and a graph neural network. This innovative approach enables the concurrent analysis of temporal and spatial features of wind energy, significantly enhancing the forecasting accuracy. A pivotal feature of this model is its unique mechanism to compute the mutual influence between wind turbines, with a particular focus on the blockage effect, a key factor in turbine interactions. The model’s efficacy is validated using a real-world dataset, targeting a 48-hour prediction horizon. The experimental outcomes demonstrate that this model achieves superior performance compared to state-of-the-art methods, with a notable improvement of 6.87% in Root Mean Square Error and 8.77% in Mean Absolute Error. This study not only highlights the model’s enhanced forecasting capabilities but also emphasizes the importance of integrating spatial and temporal dynamics in wind farms for improving Wind Power Forecasting accuracy.

Suggested Citation

  • Qiu, Hong & Shi, Kaikai & Wang, Renfang & Zhang, Liang & Liu, Xiufeng & Cheng, Xu, 2024. "A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects," Renewable Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:renene:v:227:y:2024:i:c:s0960148124005640
    DOI: 10.1016/j.renene.2024.120499
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    References listed on IDEAS

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    1. Liu, Xin & Cao, Zheming & Zhang, Zijun, 2021. "Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning," Energy, Elsevier, vol. 217(C).
    2. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    3. Heng, Jiani & Hong, Yongmiao & Hu, Jianming & Wang, Shouyang, 2022. "Probabilistic and deterministic wind speed forecasting based on non-parametric approaches and wind characteristics information," Applied Energy, Elsevier, vol. 306(PA).
    4. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    5. Zheng, Jianqin & Du, Jian & Wang, Bohong & Klemeš, Jiří Jaromír & Liao, Qi & Liang, Yongtu, 2023. "A hybrid framework for forecasting power generation of multiple renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    6. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    7. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao & Xie, Yuying & Liu, Fangjie, 2022. "A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution," Energy, Elsevier, vol. 238(PC).
    8. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    9. Shijun Wang & Chun Liu & Kui Liang & Ziyun Cheng & Xue Kong & Shuang Gao, 2022. "Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    10. Hermesmann, M. & Grübel, K. & Scherotzki, L. & Müller, T.E., 2021. "Promising pathways: The geographic and energetic potential of power-to-x technologies based on regeneratively obtained hydrogen," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    11. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    12. Wang, Lin & Tao, Rui & Hu, Huanling & Zeng, Yu-Rong, 2021. "Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder," Renewable Energy, Elsevier, vol. 164(C), pages 642-655.
    13. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    14. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    15. 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.
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