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Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges

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  1. Chen, Wenhe & Zhou, Hanting & Cheng, Longsheng & Xia, Min, 2023. "Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention," Energy, Elsevier, vol. 278(PB).
  2. Meng, Anbo & Zhang, Haitao & Dai, Zhongfu & Xian, Zikang & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhu, Jianbin & Li, Hanhong & Yin, Yiding & Liu, Jiawei & Tang, Yanshu & Zhang, Bin & Yin, Hao, 2024. "An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division," Energy, Elsevier, vol. 299(C).
  3. Kirchner-Bossi, Nicolas & Kathari, Gabriel & Porté-Agel, Fernando, 2024. "A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space," Applied Energy, Elsevier, vol. 367(C).
  4. Zhang, Chao & Yin, Wanjun & Wen, Tao, 2024. "An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid," Energy, Elsevier, vol. 287(C).
  5. Yang, Mao & Huang, Yutong & Xu, Chuanyu & Liu, Chenyu & Dai, Bozhi, 2025. "Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations," Applied Energy, Elsevier, vol. 377(PC).
  6. Upasana Lakhina & Nasreen Badruddin & Irraivan Elamvazuthi & Ajay Jangra & Truong Hoang Bao Huy & Josep M. Guerrero, 2023. "An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
  7. Zhang, Dongdong & Chen, Baian & Zhu, Hongyu & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model," Energy, Elsevier, vol. 285(C).
  8. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  9. Lu, Peng & Yang, Jianbin & Ye, Lin & Zhang, Ning & Wang, Yaqing & Di, Jingyi & Gao, Ze & Wang, Cheng & Liu, Mingyang, 2024. "A novel adaptively combined model based on induced ordered weighted averaging for wind power forecasting," Renewable Energy, Elsevier, vol. 226(C).
  10. Ding, Jun-Wei & Chuang, Ming-Ju & Tseng, Jing-Siou & Hsieh, I-Yun Lisa, 2024. "Reanalysis and Ground Station data: Advanced data preprocessing in deep learning for wind power prediction," Applied Energy, Elsevier, vol. 375(C).
  11. Christopher Jung, 2024. "Recent Development and Future Perspective of Wind Power Generation," Energies, MDPI, vol. 17(21), pages 1-5, October.
  12. Bashar Abbas Fadheel & Noor Izzri Abdul Wahab & Ali Jafer Mahdi & Manoharan Premkumar & Mohd Amran Bin Mohd Radzi & Azura Binti Che Soh & Veerapandiyan Veerasamy & Andrew Xavier Raj Irudayaraj, 2023. "A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System," Energies, MDPI, vol. 16(3), pages 1-28, January.
  13. Umme Mumtahina & Sanath Alahakoon & Peter Wolfs, 2024. "Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review," Mathematics, MDPI, vol. 12(21), pages 1-51, October.
  14. Mahsa Dehghan Manshadi & Milad Mousavi & M. Soltani & Amir Mosavi & Levente Kovacs, 2022. "Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System," Energies, MDPI, vol. 15(24), pages 1-16, December.
  15. Ye, Lin & Dai, Binhua & Li, Zhuo & Pei, Ming & Zhao, Yongning & Lu, Peng, 2022. "An ensemble method for short-term wind power prediction considering error correction strategy," Applied Energy, Elsevier, vol. 322(C).
  16. Zilong, Ti & Yubing, Song & Xiaowei, Deng, 2022. "Spatial-temporal wave height forecast using deep learning and public reanalysis dataset," Applied Energy, Elsevier, vol. 326(C).
  17. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
  18. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
  19. Niu, Dongxiao & Sun, Lijie & Yu, Min & Wang, Keke, 2022. "Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model," Energy, Elsevier, vol. 254(PA).
  20. 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.
  21. 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).
  22. 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).
  23. Meng, Anbo & Chen, Shu & Ou, Zuhong & Xiao, Jianhua & Zhang, Jianfeng & Chen, Shun & Zhang, Zheng & Liang, Ruduo & Zhang, Zhan & Xian, Zikang & Wang, Chenen & Yin, Hao & Yan, Baiping, 2022. "A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network," Energy, Elsevier, vol. 261(PA).
  24. Ai, Chunyu & He, Shan & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power prediction method based on OVMD-PE and improved multi-objective state transition algorithm," Energy, Elsevier, vol. 278(C).
  25. Yang, Guoming & Yang, Dazhi & Liu, Bai & Zhang, Hao, 2024. "The role of short- and long-duration energy storage in reducing the cost of firm photovoltaic generation," Applied Energy, Elsevier, vol. 374(C).
  26. 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.
  27. Chen, Fuhao & Yan, Jie & Liu, Yongqian & Yan, Yamin & Tjernberg, Lina Bertling, 2024. "A novel meta-learning approach for few-shot short-term wind power forecasting," Applied Energy, Elsevier, vol. 362(C).
  28. Kui Yang & Bofu Wang & Xiang Qiu & Jiahua Li & Yuze Wang & Yulu Liu, 2022. "Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit," Energies, MDPI, vol. 15(12), pages 1-24, June.
  29. Gao, Jiaxin & Cheng, Yuanqi & Zhang, Dongxiao & Chen, Yuntian, 2025. "Physics-constrained wind power forecasting aligned with probability distributions for noise-resilient deep learning," Applied Energy, Elsevier, vol. 383(C).
  30. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
  31. liu, Qian & li, Yulin & jiang, Hang & chen, Yilin & zhang, Jiang, 2024. "Short-term photovoltaic power forecasting based on multiple mode decomposition and parallel bidirectional long short term combined with convolutional neural networks," Energy, Elsevier, vol. 286(C).
  32. Safae Rbihou & Khalid Haddouch & Karim El moutaouakil, 2024. "Optimizing hyperparameters in Hopfield neural networks using evolutionary search," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1245-1273, September.
  33. Zhengxuan Xiao & Fei Tang & Mengyuan Wang, 2023. "Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
  34. Kerschbaum, Alina & Trentmann, Lennart & Hanel, Andreas & Fendt, Sebastian & Spliethoff, Hartmut, 2025. "Methods for analysing renewable energy potentials in energy system modelling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 215(C).
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