IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v322y2022ics0306261922008017.html

An ensemble method for short-term wind power prediction considering error correction strategy

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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).
  2. 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).
  3. Qiu, Lihong & Ma, Wentao & Feng, Xiaoyang & Dai, Jiahui & Dong, Yuzhuo & Duan, Jiandong & Chen, Badong, 2024. "A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique," Applied Energy, Elsevier, vol. 359(C).
  4. Ling Miao & Ning Zhou & Jianwei Ma & Hao Liu & Jian Zhao & Xiaozhao Wei & Jingyuan Yin, 2025. "Current Status, Challenges and Future Perspectives of Operation Optimization, Power Prediction and Virtual Synchronous Generator of Microgrids: A Comprehensive Review," Energies, MDPI, vol. 18(13), pages 1-41, July.
  5. Jin, Ji & Tian, Jinyu & Yu, Min & Wu, Yong & Tang, Yuanyan, 2024. "A novel ultra-short-term wind speed prediction method based on dynamic adaptive continued fraction," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
  6. Yuan Sun & Shiyang Zhang, 2024. "A Multiscale Hybrid Wind Power Prediction Model Based on Least Squares Support Vector Regression–Regularized Extreme Learning Machine–Multi-Head Attention–Bidirectional Gated Recurrent Unit and Data Decomposition," Energies, MDPI, vol. 17(12), pages 1-21, June.
  7. Yang, Mao & Jiang, Renxian & Wang, Bo & Fang, Guozhong & Jia, Yunpeng & Fan, Fulin, 2025. "Multi-channel attention mechanism graph convolutional network considering cumulative effect and temporal causality for day-ahead wind power prediction," Energy, Elsevier, vol. 332(C).
  8. Chong Wu & Tong Xu & Shenhao Yang & Yong Zheng & Xiaobin Yan & Maoyu Mao & Ziyi Jiang & Qian Li, 2025. "Gas−Hydro Coordinated Peaking Considering Source-Load Uncertainty and Deep Peaking," Energies, MDPI, vol. 18(5), pages 1-23, March.
  9. Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
  10. Zhang, Chu & Tao, Zihan & Xiong, Jinlin & Qian, Shijie & Fu, Yongyan & Ji, Jie & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Research and application of a novel weight-based evolutionary ensemble model using principal component analysis for wind power prediction," Renewable Energy, Elsevier, vol. 232(C).
  11. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Ouassaid, Mohammed, 2023. "Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window," Energy, Elsevier, vol. 263(PE).
  12. Zifa Liu & Xinyi Li & Haiyan Zhao, 2023. "Short-Term Wind Power Forecasting Based on Feature Analysis and Error Correction," Energies, MDPI, vol. 16(10), pages 1-24, May.
  13. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
  14. Xin He & Yichen Ma & Jiancang Xie & Gang Zhang & Tuo Xie, 2025. "Enhanced Wind Power Forecasting Using Graph Convolutional Networks with Ramp Characterization and Error Correction," Energies, MDPI, vol. 18(11), pages 1-22, May.
  15. Ye, Lin & Li, Yilin & Pei, Ming & Zhao, Yongning & Li, Zhuo & Lu, Peng, 2022. "A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching," Applied Energy, Elsevier, vol. 327(C).
  16. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
  17. 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).
  18. Liu, Xiaoyan & Zhen, Zhao & Mi, Zengqiang & Hao, Ling & Xu, Fei & Wang, Fei, 2026. "Two-stage ultra-short-term wind power forecasting based on multi-scale wind process extraction and fluctuation continuation analysis," Energy, Elsevier, vol. 342(C).
  19. Zhao, Ning & Su, Yi & Dai, Xianxing & Jia, Shaomin & Wang, Xuewei, 2024. "A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed prediction," Applied Energy, Elsevier, vol. 369(C).
  20. 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).
  21. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update," Applied Energy, Elsevier, vol. 340(C).
  22. 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).
  23. Xuehui Wang & Yongsheng Wang & Yongsheng Qi & Jiajing Gao & Fan Yang & Jiaxuan Lu, 2025. "An Ultra-Short-Term Wind Power Prediction Method Based on the Fusion of Multiple Technical Indicators and the XGBoost Algorithm," Energies, MDPI, vol. 18(12), pages 1-21, June.
  24. 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).
  25. Huang, Jing & Qin, Rui, 2024. "Elman neural network considering dynamic time delay estimation for short-term forecasting of offshore wind power," Applied Energy, Elsevier, vol. 358(C).
  26. Jian Liu & Xiaotian He & Kangji Li & Wenping Xue, 2025. "A Review of State-of-the-Art AI and Data-Driven Techniques for Load Forecasting," Energies, MDPI, vol. 18(16), pages 1-27, August.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.