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M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions

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  • Wang, Lei
  • He, Yigang

Abstract

In recent years, wind power has continued to emerge as a key source of renewable energy. When large-scale wind farm clusters are connected to the grid for power generation, accurate multi-location ultra-short-term wind power predictions carry significant value in terms of ensuring the safety, stability, and economical operation of the power system. However, there are complex temporal and spatial correlations among multiple wind farms in multiple locations, which makes wind power predictions involving wind farm clusters very challenging. The development of artificial intelligence technology, especially graph machine learning, provides new approaches for modeling such spatiotemporal correlations. In addition, compared with single-step forecasting, multi-step forecasting can better reflect the general situation, and thus, it is more widely applicable in reality. To optimize multi-step wind power predictions in multiple locations, this report proposes a Multi-Modal Multi-Task Spatiotemporal Attention Network (M2STAN) model. The developed model employs a graph attention network and a bidirectional gated recurrent unit (Bi-GRU) to model the spatial and temporal dependence, respectively. In addition, the introduction of multi-modal and multi-task learning strategies improves the accuracy and computational efficiency of this predictive model. The results indicate that the proposed method is superior to existing methods, including support vector regression, Bi-GRU, multi-modal multi-task graph spatiotemporal networks, and graph convolutional deep learning architectures in terms of prediction performance.

Suggested Citation

  • Wang, Lei & He, Yigang, 2022. "M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions," Applied Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922009709
    DOI: 10.1016/j.apenergy.2022.119672
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    8. Peng, Cheng & Zhang, Yiqin & Zhang, Bowen & Song, Dan & Lyu, Yi & Tsoi, AhChung, 2023. "A novel ultra-short-term wind power prediction method based on XA mechanism," Applied Energy, Elsevier, vol. 351(C).
    9. 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).
    10. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    11. 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).
    12. 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).
    13. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.
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