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A short-term wind power prediction model based on CEEMD and WOA-KELM

Citations

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Cited by:

  1. Lu, Quan & Huang, Wenxuan & Yin, Linfei, 2025. "Decomposition prediction fractional-order active disturbance rejection control deep Q network for generation control of integrated energy systems," Applied Energy, Elsevier, vol. 377(PD).
  2. Long, Jian & Huang, Cheng & Deng, Kai & Wan, Lei & Hu, Guihua & Zhang, Feng, 2024. "Novel hybrid data-driven modeling integrating variational modal decomposition and dual-stage self-attention model: Applied to industrial petrochemical process," Energy, Elsevier, vol. 304(C).
  3. 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).
  4. Fang, Mingkun & Zhang, Fangfang & Zhu, Di & Tao, Ran & Xiao, Ruofu, 2025. "The influence of variational mode decomposition on LSTM prediction accuracy - A case study with wind turbine power signals," Renewable Energy, Elsevier, vol. 245(C).
  5. Yingying He & Likai Zhang & Tengda Guan & Zheyu Zhang, 2024. "An Integrated CEEMDAN to Optimize Deep Long Short-Term Memory Model for Wind Speed Forecasting," Energies, MDPI, vol. 17(18), pages 1-29, September.
  6. 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).
  7. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
  8. Zheng, Xidong & Bai, Feifei & Zhuang, Zhiyuan & Chen, Zixing & Jin, Tao, 2023. "A new demand response management strategy considering renewable energy prediction and filtering technology," Renewable Energy, Elsevier, vol. 211(C), pages 656-668.
  9. Yang, Mao & Guo, Yunfeng & Fan, Fulin & Huang, Tao, 2024. "Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering," Energy, Elsevier, vol. 302(C).
  10. 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.
  11. Zeng, Liling & Hu, Huanling & Song, Qingkui & Zhang, Boting & Lin, Ruibin & Zhang, Dabin, 2024. "A drift-aware dynamic ensemble model with two-stage member selection for carbon price forecasting," Energy, Elsevier, vol. 313(C).
  12. ZeSheng Lin, 2025. "Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-25, January.
  13. Su, Yixin & Wang, Zeyu & Dong, Zhengcheng & Hua, Xiaojun & Ye, Tao & Song, Zida & Shao, Yun, 2025. "Frequency-aware ultra-short-term wind power forecasting using CEEMDAN–VMD–SE and Transformer–GRU networks," Energy, Elsevier, vol. 338(C).
  14. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
  15. Leal, Jairon Isaias & Pitombeira-Neto, Anselmo Ramalho & Bueno, André Valente & Costa Rocha, Paulo Alexandre & de Andrade, Carla Freitas, 2025. "Probabilistic wind speed forecasting via Bayesian DLMs and its application in green hydrogen production," Applied Energy, Elsevier, vol. 382(C).
  16. Karijadi, Irene & Chou, Shuo-Yan & Dewabharata, Anindhita, 2023. "Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method," Renewable Energy, Elsevier, vol. 218(C).
  17. Liu, Xinyi & Wang, Zitao & Xu, Shuai & Miao, Yihe & Xu, Jialing & Liu, Shanke & Yu, Lijun, 2025. "Performance analysis of wind-hydrogen energy storage system using composite objective optimization proactive scheduling strategy coordinated with wind power prediction," Energy, Elsevier, vol. 321(C).
  18. 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).
  19. Muxin Nian & Rui Dong & Weihuang Zhong & Yunhua Zhang & Diming Lou, 2025. "Multi-Objective Optimization Study on Capture Performance of Diesel Particulate Filter Based on the GRA-MLR-WOA Hybrid Method," Sustainability, MDPI, vol. 17(19), pages 1-21, September.
  20. 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).
  21. Gao, JiaJing & Xing, HongMei & Wang, YongSheng & Liu, GuangChen & Cheng, Bo & Zhang, DeLong, 2025. "Ultra-short-term wind power prediction based on hybrid denoising with improved CEEMD decomposition," Renewable Energy, Elsevier, vol. 251(C).
  22. Yu, Chunsheng, 2025. "A comprehensive wind power prediction system based on correct multiscale clustering ensemble, similarity matching, and improved whale optimization algorithm—A case study in China," Renewable Energy, Elsevier, vol. 243(C).
  23. Liang, Yang & Zhang, Dongqin & Zhang, Jize & Hu, Gang, 2024. "A state-of-the-art analysis on decomposition method for short-term wind speed forecasting using LSTM and a novel hybrid deep learning model," Energy, Elsevier, vol. 313(C).
  24. Zheng, Xidong & Chen, Huangbin & Jin, Tao, 2024. "A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province," Renewable Energy, Elsevier, vol. 220(C).
  25. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
  26. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
  27. Zhang, Wanying & He, Yaoyao & Yang, Shanlin, 2023. "A multi-step probability density prediction model based on gaussian approximation of quantiles for offshore wind power," Renewable Energy, Elsevier, vol. 202(C), pages 992-1011.
  28. Lin, Qingcheng & Cai, Huiling & Liu, Hanwei & Li, Xuefeng & Xiao, Hui, 2024. "A novel ultra-short-term wind power prediction model jointly driven by multiple algorithm optimization and adaptive selection," Energy, Elsevier, vol. 288(C).
  29. Sun, Xiaoying & Liu, Haizhong, 2024. "Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S," Energy, Elsevier, vol. 305(C).
  30. Shi, Jian & Teh, Jiashen & Lai, Ching-Ming, 2025. "Wind power prediction based on improved self-attention mechanism combined with Bi-directional Temporal Convolutional Network," Energy, Elsevier, vol. 322(C).
  31. Suo, Leiming & Peng, Tian & Song, Shihao & Zhang, Chu & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad, 2023. "Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm," Energy, Elsevier, vol. 276(C).
  32. 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).
  33. Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023. "A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective of China," Energy, Elsevier, vol. 283(C).
  34. Wang, Chao & Lin, Hong & Yang, Ming & Fu, Xiaoling & Yuan, Yue & Wang, Zewei, 2024. "A novel chaotic time series wind power point and interval prediction method based on data denoising strategy and improved coati optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
  35. Xiwen Cui & Xinyu Guan & Dongyu Wang & Dongxiao Niu & Xiaomin Xu, 2022. "Can China Meet Its 2030 Total Energy Consumption Target? Based on an RF-SSA-SVR-KDE Model," Energies, MDPI, vol. 15(16), pages 1-13, August.
  36. Shi, Jian & Teh, Jiashen & Alharbi, Bader & Lai, Ching-Ming, 2024. "Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model," Energy, Elsevier, vol. 297(C).
  37. Sun, Shaolong & Du, Zongjuan & Jin, Kun & Li, Hongtao & Wang, Shouyang, 2023. "Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy," Applied Energy, Elsevier, vol. 350(C).
  38. Dong, Yunxuan & Zhou, Binggui & Zhang, Hongcai & Yang, Guanghua & Ma, Shaodan, 2026. "A deep time–frequency augmented wind power forecasting model," Renewable Energy, Elsevier, vol. 256(PA).
  39. Fu, Wenlong & Shao, Mengxin & Zhu, Xinfeng & Zheng, Bo & Liao, Xiang & Mei, Qicheng & Li, Shuai & Xiong, Haowei, 2025. "Dual-path ultra-short-term wind power forecasting based on numerical weather prediction and multi-order temporal dynamic gating fusion," Energy, Elsevier, vol. 335(C).
  40. Cui, Xiwen & Yu, Xiaoyu & Niu, Dongxiao, 2024. "The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm and attention mechanism," Energy, Elsevier, vol. 288(C).
  41. Xiao, Yiping & Wei, Honghao & Shi, Ying & Zhang, Haiyang & Shen, Zongtao & Jiao, Hongjian, 2025. "A short-term wind power prediction based on MCOOT optimized deep learning networks and attention-weighted environmental factors for error correction," Energy, Elsevier, vol. 324(C).
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