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Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method

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. Liu, Jiarui & Fu, Yuchen, 2023. "Renewable energy forecasting: A self-supervised learning-based transformer variant," Energy, Elsevier, vol. 284(C).
  3. Cai, Yizhuo & Li, Yanting, 2024. "Short-term wind speed forecast based on dynamic spatio-temporal directed graph attention network," Applied Energy, Elsevier, vol. 375(C).
  4. Yubo Wang & Chao Huo & Fei Xu & Libin Zheng & Ling Hao, 2025. "Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling," Energies, MDPI, vol. 18(1), pages 1-21, January.
  5. Zhao, Beizhen & He, Xin & Ran, Shaolin & Zhang, Yong & Cheng, Cheng, 2024. "Spatial correlation learning based on graph neural network for medium-term wind power forecasting," Energy, Elsevier, vol. 296(C).
  6. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
  7. Haonan Dai & Yumo Zhang & Fei Wang, 2025. "A Day-Ahead PV Power Forecasting Method Based on Irradiance Correction and Weather Mode Reliability Decision," Energies, MDPI, vol. 18(11), pages 1-19, May.
  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. Wang, Sen & Sun, Yonghui & Zhang, Wenjie & Chung, C.Y. & Srinivasan, Dipti, 2024. "Very short-term wind power forecasting considering static data: An improved transformer model," Energy, Elsevier, vol. 312(C).
  10. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Huang, Yuehui & Chen, Jianjun & Li, Yahe & Chen, Zhe & Blaabjerg, Frede, 2024. "Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms," Applied Energy, Elsevier, vol. 355(C).
  11. Chen, Yuejiang & Xiao, Jiang-Wen & Wang, Yan-Wu & Luo, Yunfeng, 2025. "Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation," Applied Energy, Elsevier, vol. 377(PA).
  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. Qu, Zhijian & Hou, Xinxing & Li, Jian & Hu, Wenbo, 2024. "Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation," Energy, Elsevier, vol. 290(C).
  14. 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).
  15. Niu, Zhewen & Han, Xiaoqing & Zhang, Dongxia & Wu, Yuxiang & Lan, Songyan, 2024. "Interpretable wind power forecasting combining seasonal-trend representations learning with temporal fusion transformers architecture," Energy, Elsevier, vol. 306(C).
  16. Chen, Xia & Zhang, Huimin & Zhao, Xin Xin & Gong, Qiang & Chang, Chun-Ping, 2024. "Do renewable energy investment and financial development mitigate climate change?," Journal of Asian Economics, Elsevier, vol. 93(C).
  17. Verdone, Alessio & Panella, Massimo & De Santis, Enrico & Rizzi, Antonello, 2025. "A review of solar and wind energy forecasting: From single-site to multi-site paradigm," Applied Energy, Elsevier, vol. 392(C).
  18. Chen, Shi & Li, Chuangzhi & Zang, Tianlei & Zhou, Buxiang & Yang, Lonjie & Qiu, Yiwei & Zhou, Yi & Zhang, Xiaoshun, 2024. "Multi-timescale dispatch technology for islanded energy system in the Gobi Desert," Renewable Energy, Elsevier, vol. 234(C).
  19. He Yin & Hai Lan & Ying-Yi Hong & Zhuangwei Wang & Peng Cheng & Dan Li & Dong Guo, 2023. "A Comprehensive Review of Shipboard Power Systems with New Energy Sources," Energies, MDPI, vol. 16(5), pages 1-44, February.
  20. Xin Zhao & Qiushuang Li & Wanlei Xue & Yihang Zhao & Huiru Zhao & Sen Guo, 2022. "Research on Ultra-Short-Term Load Forecasting Based on Real-Time Electricity Price and Window-Based XGBoost Model," Energies, MDPI, vol. 15(19), pages 1-11, October.
  21. Qu, Zhijian & Hou, Xinxing & Huang, ShiXun & Li, Di & He, Yang & Meng, Yan, 2025. "Probabilistic power forecasting for wind farm clusters using Moran-Graph network with posterior feedback attention mechanism," Energy, Elsevier, vol. 328(C).
  22. Liu, Jiarui & Fu, Yuchen, 2023. "Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction," Energy, Elsevier, vol. 284(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. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
  26. Fan, Huijing & Zhen, Zhao & Liu, Nian & Sun, Yiqian & Chang, Xiqiang & Li, Yu & Wang, Fei & Mi, Zengqiang, 2023. "Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method," Energy, Elsevier, vol. 266(C).
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