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Short-term wind speed forecasting based on spatial-temporal graph transformer networks

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  • Pan, Xiaoxin
  • Wang, Long
  • Wang, Zhongju
  • Huang, Chao

Abstract

Wind energy is a widely concerned renewable energy source. Accurate short-term wind speed forecasting is helpful for the stable operation of wind power systems, which is crucial to the wind power industry. In this paper, a Spatial-Temporal Graph Transformer Network (STGTN) is proposed to improve the performance of short-term wind speed forecasting. The proposed model consists of a temporal feature extraction module and a spatial feature extraction module and thus it can capture the temporal and spatial correlations between wind turbine nodes. A transformer based on the external attention mechanism and the graph convolutional layer is proposed to extract spatial features while a multilayer perceptron is employed to derive temporal features. Since the graph convolutional layer relies on the Euclidean spatial topology input, the location distribution of wind turbine nodes is not considered in the proposed model. To verify the performance of the STGTN model, five wind speed forecasting methods (with and without spatial dependencies) are employed as benchmarks. Experimental results show that the proposed model performs the best in terms of the mean absolute error, root mean square error and mean absolute percentage error for each forecasting horizon.

Suggested Citation

  • Pan, Xiaoxin & Wang, Long & Wang, Zhongju & Huang, Chao, 2022. "Short-term wind speed forecasting based on spatial-temporal graph transformer networks," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222009987
    DOI: 10.1016/j.energy.2022.124095
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    References listed on IDEAS

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    Citations

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    Cited by:

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    5. Lin, Shengmao & Wang, Shu & Xu, Xuefang & Li, Ruixiong & Shi, Peiming, 2024. "GAOformer: An adaptive spatiotemporal feature fusion transformer utilizing GAT and optimizable graph matrixes for offshore wind speed prediction," Energy, Elsevier, vol. 292(C).
    6. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "Dual-meta pool method for wind farm power forecasting with small sample data," Energy, Elsevier, vol. 267(C).
    7. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting," Energy, Elsevier, vol. 282(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. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    10. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    11. Zhang, Dongqin & Hu, Gang & Song, Jie & Gao, Huanxiang & Ren, Hehe & Chen, Wenli, 2024. "A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model," Energy, Elsevier, vol. 288(C).

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