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Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks

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  • Wu, Qiang
  • Zheng, Hongling
  • Guo, Xiaozhu
  • Liu, Guangqiang

Abstract

Wind power has become one of the essential solutions to renewable energy and sustainable development problems. The accuracy of wind speed forecasts primarily determines the utilization of wind energy. Because many factors affect wind speed, high-precision wind speed prediction is still a practical and challenging task. In this paper, we propose multidimensional spatial-temporal graph neural networks (MST-GNN) for wind speed prediction: (1) establish a transformer-based model named Wind-Transformer on temporal perspective for single-point wind speed prediction with multidimensional data (wind speed, wind direction, temperature, air pressure, etc.), (2) apply graph neural network using Wind-Transformer as a node on spatial view to accurately predict the wind speed at local point by comprehensively aggregating the wind speed of local point and surrounding points. Through comprehensive experiments on open source datasets for wind speed prediction, we demonstrate that our model MST-GNN outperforms the state-of-the-art baselines up to 8.96%. The longer the prediction steps, the more improvement relative to other methods.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:199:y:2022:i:c:p:977-992
    DOI: 10.1016/j.renene.2022.09.036
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    7. Chen, Zhengganzhe & Zhang, Bin & Du, Chenglong & Meng, Wei & Meng, Anbo, 2024. "A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction," Energy, Elsevier, vol. 294(C).
    8. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network," Energy, Elsevier, vol. 285(C).
    9. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
    10. Jiang, Wenjun & Liu, Bo & Liang, Yang & Gao, Huanxiang & Lin, Pengfei & Zhang, Dongqin & Hu, Gang, 2024. "Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables," Applied Energy, Elsevier, vol. 353(PB).
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