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Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty

Citations

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  1. Cheng, Fang & Liu, Hui, 2024. "Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks," Applied Energy, Elsevier, vol. 376(PB).
  2. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
  3. Jiajian Ke & Tian Chen, 2024. "Data Decomposition Modeling Based on Improved Dung Beetle Optimization Algorithm for Wind Power Prediction," Data, MDPI, vol. 9(12), pages 1-23, December.
  4. Jin, Huaiping & Zhang, Kehao & Fan, Shouyuan & Jin, Huaikang & Wang, Bin, 2024. "Wind power forecasting based on ensemble deep learning with surrogate-assisted evolutionary neural architecture search and many-objective federated learning," Energy, Elsevier, vol. 308(C).
  5. Yi Liu & Jun He & Yu Wang & Zong Liu & Lixun He & Yanyang Wang, 2023. "Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain," Energies, MDPI, vol. 16(14), pages 1-25, July.
  6. G. Ponkumar & S. Jayaprakash & Karthick Kanagarathinam, 2023. "Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis," Energies, MDPI, vol. 16(14), pages 1-24, July.
  7. Xu, Xin & Cao, Qinglong & Deng, Ruizhe & Guo, Zhiling & Chen, Yuntian & Yan, Jinyue, 2025. "A cross-dataset benchmark for neural network-based wind power forecasting," Renewable Energy, Elsevier, vol. 254(C).
  8. Yao, Xianshuang & Guo, Kangshuai & Lei, Jianqi & Li, Xuanyu, 2024. "Fully connected multi-reservoir echo state networks for wind power prediction," Energy, Elsevier, vol. 312(C).
  9. 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).
  10. Wang, Cong & He, Yan & Zhang, Hong-li & Ma, Ping, 2024. "Wind power forecasting based on manifold learning and a double-layer SWLSTM model," Energy, Elsevier, vol. 290(C).
  11. Zhao, Dan & He, Hongying & Luo, Diansheng & Huang, Shoudao & Zhang, Zihan, 2025. "Ultra-short-term wind power forecast based on multi-feature information fusion of GAT-Crossformer," Energy, Elsevier, vol. 340(C).
  12. Liu, Tianhao & Shan, Linke & Jiang, Meihui & Li, Fangning & Kong, Fannie & Du, Pengcheng & Zhu, Hongyu & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Zhang, Dongdong, 2025. "Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation," Applied Energy, Elsevier, vol. 398(C).
  13. Mi, Lihua & Han, Yan & Long, Lizhi & Chen, Hui & Cai, C.S., 2025. "A physics-informed temporal convolutional network-temporal fusion transformer hybrid model for probabilistic wind speed predictions with quantile regression," Energy, Elsevier, vol. 326(C).
  14. Liu, Yi & Wang, Ranpeng & Gu, Yin & Li, Congjian & Wang, Gangqiao, 2024. "Physics-inspired and data-driven two-stage deep learning approach for wind field reconstruction with experimental validation," Energy, Elsevier, vol. 298(C).
  15. Qingquan Lv & Jialin Zhang & Jianmei Zhang & Zhenzhen Zhang & Qiang Zhou & Pengfei Gao & Haozhe Zhang, 2025. "Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM," Energies, MDPI, vol. 18(13), pages 1-18, June.
  16. Yingrui Chen & Jiarong Shi, 2025. "Broad Random Forest: A Lightweight Prediction Model for Short-Term Wind Power by Fusing Broad Learning and Random Forest," Sustainability, MDPI, vol. 17(11), pages 1-16, May.
  17. Wang, Sen & Sun, Yonghui & Zhang, Wenjie & Srinivasan, Dipti, 2025. "Optimization of deterministic and probabilistic forecasting for wind power based on ensemble learning," Energy, Elsevier, vol. 319(C).
  18. Liu, Wei & Wang, Xian & Long, Qingcan & Zeng, Bing & Zhong, Shuai, 2025. "A sensitive and easy-to-deploy condition monitoring method for main drive chain of large wind turbines," Renewable Energy, Elsevier, vol. 254(C).
  19. Hong, Jun-Tao & Han, Shuang & Yan, Jie & Liu, Yong-Qian, 2025. "Dual-path frequency Mamba-Transformer model for wind power forecasting," Energy, Elsevier, vol. 332(C).
  20. Te Li & Mengze Zhang & Yan Zhou, 2024. "LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting," Papers 2410.15286, arXiv.org.
  21. Chen, Yuejiang & He, Yingjing & Xiao, Jiang-Wen & Wang, Yan-Wu & Li, Yuanzheng, 2024. "Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting," Energy, Elsevier, vol. 304(C).
  22. Liu, Lei & Wang, Xinyu & Dong, Xue & Chen, Kang & Chen, Qiuju & Li, Bin, 2024. "Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series," Applied Energy, Elsevier, vol. 374(C).
  23. Wang, Shuangxin & Shi, Jiarong & Yang, Wei & Yin, Qingyan, 2024. "High and low frequency wind power prediction based on Transformer and BiGRU-Attention," Energy, Elsevier, vol. 288(C).
  24. Zhang, Yagang & Kong, Xue & Wang, Jingchao & Wang, Hui & Cheng, Xiaodan, 2024. "Wind power forecasting system with data enhancement and algorithm improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
  25. Zongxu Liu & Hui Guo & Yingshuai Zhang & Zongliang Zuo, 2025. "A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges," Energies, MDPI, vol. 18(2), pages 1-17, January.
  26. Xiao, Xiao & Zhang, Xuan & Song, Meiqi & Liu, Xiaojing & Huang, Qingyu, 2024. "NPP accident prevention: Integrated neural network for coupled multivariate time series prediction based on PSO and its application under uncertainty analysis for NPP data," Energy, Elsevier, vol. 305(C).
  27. Li, Yang & Zhao, Bingsong & Li, Yuanzheng & Long, Chao & Li, Sen & Dong, Zhaoyang & Shahidehpour, Mohammad, 2025. "Safe-AutoSAC: AutoML-enhanced safe deep reinforcement learning for integrated energy system scheduling with multi-channel informer forecasting and electric vehicle demand response," Applied Energy, Elsevier, vol. 399(C).
  28. Chen, Congcong & Chai, Lin & Wang, Qingling, 2025. "A wind power ultra-short-term ensemble forecast framework considering wind speed correction and scenario classification," Energy, Elsevier, vol. 336(C).
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