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

Citations

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Cited by:

  1. Bentsen, Lars Ødegaard & Warakagoda, Narada Dilp & Stenbro, Roy & Engelstad, Paal, 2023. "Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures," Applied Energy, Elsevier, vol. 333(C).
  2. Zhao, Yongning & Pan, Shiji & Zhao, Yuan & Liao, Haohan & Ye, Lin & Zheng, Yingying, 2024. "Ultra-short-term wind power forecasting based on personalized robust federated learning with spatial collaboration," Energy, Elsevier, vol. 288(C).
  3. Wang, Zhongrui & Wang, Chunbo & Chen, Liang & Yu, Min & Yuan, Wenteng, 2025. "Short-term offshore wind power multi-location multi-modal multi-step prediction model based on Informer (M3STIN)," Energy, Elsevier, vol. 322(C).
  4. 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).
  5. Chuan Long & Xinting Yang & Yunche Su & Fang Liu & Ruiguang Ma & Tiannan Ma & Yangjin Wu & Xiaodong Shen, 2025. "Air Conditioning Load Forecasting for Geographical Grids Using Deep Reinforcement Learning and Density-Based Spatial Clustering of Applications with Noise and Graph Attention Networks," Energies, MDPI, vol. 18(11), pages 1-19, May.
  6. Li, HongYang & He, Shan & Yuan, JiaWang & Wang, Chao, 2025. "A wind power prediction method integrating dynamic multi-scale spatio-temporal modelling, adaptive multi-strategy local decomposition, and meta-learning ensemble model," Energy, Elsevier, vol. 340(C).
  7. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhou, Qingyu & Fan, Hang, 2023. "Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction," Applied Energy, Elsevier, vol. 336(C).
  8. Chen, Juntao & Fu, Xueying & Zhang, Lingli & Shen, Haoye & Wu, Jibo, 2024. "A novel offshore wind power prediction model based on TCN-DANet-sparse transformer and considering spatio-temporal coupling in multiple wind farms," Energy, Elsevier, vol. 308(C).
  9. Wu, Tangjie & Ling, Qiang, 2024. "Self-supervised dynamic stochastic graph network for spatio-temporal wind speed forecasting," Energy, Elsevier, vol. 304(C).
  10. Wang, Da & Yang, Mao & Zhang, Wei & Ma, Chenglian & Su, Xin, 2025. "Short-term power prediction method of wind farm cluster based on deep spatiotemporal correlation mining," Applied Energy, Elsevier, vol. 380(C).
  11. 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).
  12. 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).
  13. Ke, Xue & Wang, Lei & Wang, Jun & Wang, Anyang & Wang, Ruilin & Liu, Peng & Li, Li & Han, Rong & Yin, Yiheng & Wang, Feng Ryan & Kuai, Chunguang & Guo, Yuzheng, 2025. "Battery intelligent temperature warning model with physically-informed attention residual networks," Applied Energy, Elsevier, vol. 388(C).
  14. Wang, Li & Gao, Jinhan & Li, Yunchao & Wang, Da, 2025. "A method for ultra-short-term wind power forecasting of large-scale wind farms based on adaptive spatiotemporal graph convolution," Renewable Energy, Elsevier, vol. 249(C).
  15. Xu, Xuefang & Hu, Shiting & Shao, Huaishuang & Shi, Peiming & Li, Ruixiong & Li, Deguang, 2023. "A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm," Energy, Elsevier, vol. 284(C).
  16. Fan Li & Hongzhen Wang & Dan Wang & Dong Liu & Ke Sun, 2025. "A Review of Wind Power Prediction Methods Based on Multi-Time Scales," Energies, MDPI, vol. 18(7), pages 1-47, March.
  17. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
  18. Gao, Huanxiang & Hu, Gang & Zhang, Dongqin & Jiang, Wenjun & Ren, Hehe & Chen, Wenli, 2024. "Prediction of wind fields in mountains at multiple elevations using deep learning models," Applied Energy, Elsevier, vol. 353(PA).
  19. Yu, Min & Niu, Dongxiao & Zhao, Jinqiu & Li, Mingyu & Sun, Lijie & Yu, Xiaoyu, 2023. "Building cooling load forecasting of IES considering spatiotemporal coupling based on hybrid deep learning model," Applied Energy, Elsevier, vol. 349(C).
  20. 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).
  21. Wang, Han & Li, Yunzhou & Yan, Jie & Xiao, Wuyang & Han, Shuang & Liu, Yongqian, 2025. "A novel minute-scale prediction method of incoming wind conditions with limited LiDAR data," Renewable Energy, Elsevier, vol. 240(C).
  22. 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).
  23. Yang, Mao & Jiang, Yuxi & Xu, Chuanyu & Wang, Bo & Wang, Zhao & Su, Xin, 2025. "Day-ahead wind farm cluster power prediction based on trend categorization and spatial information integration model," Applied Energy, Elsevier, vol. 388(C).
  24. 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).
  25. Wu, Xinning & Zhan, Haolin & Hu, Jianming & Wang, Ying, 2025. "Non-stationary GNNCrossformer: Transformer with graph information for non-stationary multivariate Spatio-Temporal wind power data forecasting," Applied Energy, Elsevier, vol. 377(PB).
  26. Song, Enzhe & Zhang, Xinyue & Ge, Yuwei & Yao, Chong & Wang, Bo, 2025. "Parallel TCN-BiGRU architecture with dynamic attention for ship energy consumption prediction under variable navigation conditions," Energy, Elsevier, vol. 337(C).
  27. 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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