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A review of wind speed and wind power forecasting with deep neural networks

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Cited by:

  1. Servín-Campuzano, Hermelinda & Domínguez-Pérez, Valeria Monserrat & Marín-Mendoza, Pablo César & Panales-Pérez, Alexander & Fuentes-Cortés, Luis Fabián, 2024. "The role of storage in energy security performance based on diversification and concentration for distributed energy systems," Renewable Energy, Elsevier, vol. 229(C).
  2. 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).
  3. Zhao, Yongning & Pan, Shiji & Zhao, Yuan & Liao, Haohan & Ye, Lin & Zheng, Yingying, 2024. "Ultra-short-term wind power forecasting based on personalized robust federated learning with spatial collaboration," Energy, Elsevier, vol. 288(C).
  4. Panagiotis Korkidis & Anastasios Dounis, 2023. "Intelligent Fuzzy Models: WM, ANFIS, and Patch Learning for the Competitive Forecasting of Environmental Variables," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
  5. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
  6. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Fan & Hu, Qinghua, 2024. "Dynamic non-constraint ensemble model for probabilistic wind power and wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
  7. Chengqing, Yu & Guangxi, Yan & Chengming, Yu & Yu, Zhang & Xiwei, Mi, 2023. "A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks," Energy, Elsevier, vol. 263(PE).
  8. Dongran Song & Xiao Tan & Qian Huang & Li Wang & Mi Dong & Jian Yang & Solomin Evgeny, 2024. "Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023," Energies, MDPI, vol. 17(6), pages 1-22, March.
  9. Zhu, Jun-Hua & Wang, Jie-Sheng & Zheng, Yue & Zhang, Xing-Yue & Liu, Xun & Wang, Xiao-Tian & Zhang, Song-Bo, 2024. "Two-stage coevolutionary constrained multi-objective optimization algorithm for solving optimal power flow problems with wind power and FACTS devices," Renewable Energy, Elsevier, vol. 232(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. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
  12. Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
  13. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
  14. Kgothatso Makubyane & Daniel Maposa, 2024. "Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory," Forecasting, MDPI, vol. 6(4), pages 1-23, October.
  15. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
  16. Monica Borunda & Adrián Ramírez & Raul Garduno & Carlos García-Beltrán & Rito Mijarez, 2023. "Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data," Energies, MDPI, vol. 16(23), pages 1-34, December.
  17. Huu Khoa Minh Nguyen & Quoc-Dung Phan & Yuan-Kang Wu & Quoc-Thang Phan, 2023. "Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)," Energies, MDPI, vol. 16(9), pages 1-20, April.
  18. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
  19. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  20. de Azevedo Takara, Lucas & Teixeira, Ana Clara & Yazdanpanah, Hamed & Mariani, Viviana Cocco & dos Santos Coelho, Leandro, 2024. "Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning," Applied Energy, Elsevier, vol. 369(C).
  21. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
  22. Konstantinos Moustris & Dimitrios Zafirakis, 2023. "Day-Ahead Forecasting of the Theoretical and Actual Wind Power Generation in Energy-Constrained Island Systems," Energies, MDPI, vol. 16(12), pages 1-18, June.
  23. Liu, Cheng & Wang, Wei & Wang, Zhixia & Ding, Bei & Wu, Zhiqiang & Feng, Jingjing, 2024. "Data-driven modeling and fast adjustment for digital coded metasurfaces database: Application in adaptive electromagnetic energy harvesting," Applied Energy, Elsevier, vol. 365(C).
  24. Juan M. Lujano-Rojas & Rodolfo Dufo-López & Jesús Sergio Artal-Sevil & Eduardo García-Paricio, 2023. "Searching for Promisingly Trained Artificial Neural Networks," Forecasting, MDPI, vol. 5(3), pages 1-26, September.
  25. Yang, Mao & Wang, Da & Zhang, Wei, 2023. "A short-term wind power prediction method based on dynamic and static feature fusion mining," Energy, Elsevier, vol. 280(C).
  26. Zhong, Lingshu & Wu, Pan & Pei, Mingyang, 2024. "Wind power generation prediction during the COVID-19 epidemic based on novel hybrid deep learning techniques," Renewable Energy, Elsevier, vol. 222(C).
  27. Jian Zhu & Zhiyuan Zhao & Xiaoran Zheng & Zhao An & Qingwu Guo & Zhikai Li & Jianling Sun & Yuanjun Guo, 2023. "Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer," Energies, MDPI, vol. 16(22), pages 1-15, November.
  28. 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).
  29. Lars Ødegaard Bentsen & Narada Dilp Warakagoda & Roy Stenbro & Paal Engelstad, 2023. "A Unified Graph Formulation for Spatio-Temporal Wind Forecasting," Energies, MDPI, vol. 16(20), pages 1-23, October.
  30. Wang, Yun & Song, Mengmeng & Yang, Dazhi, 2024. "Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph," Energy, Elsevier, vol. 289(C).
  31. Berny Carrera & Kwanho Kim, 2024. "Comparative Analysis of Machine Learning Techniques in Predicting Wind Power Generation: A Case Study of 2018–2021 Data from Guatemala," Energies, MDPI, vol. 17(13), pages 1-27, June.
  32. Dan Zhou & Zhiwei Zou & Yangqing Dan & Chenxuan Wang & Chenyuan Teng & Yuanlong Zhu, 2025. "An Integrated Strategy for Hybrid Energy Storage Systems to Stabilize the Frequency of the Power Grid Through Primary Frequency Regulation," Energies, MDPI, vol. 18(2), pages 1-25, January.
  33. Mu, Yunfei & Xu, Yurui & Cao, Yan & Chen, Wanqing & Jia, Hongjie & Yu, Xiaodan & Jin, Xiaolong, 2022. "A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model," Applied Energy, Elsevier, vol. 323(C).
  34. Hu, Miaosen & Zheng, Guoqiang & Su, Zhonge & Kong, Lingrui & Wang, Guodong, 2024. "Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks," Energy, Elsevier, vol. 303(C).
  35. Zheng, Minglei & Man, Junfeng & Wang, Dian & Chen, Yanan & Li, Qianqian & Liu, Yong, 2023. "Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  36. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
  37. Xiaoshuang Huang & Yinbao Zhang & Jianzhong Liu & Xinjia Zhang & Sicong Liu, 2023. "A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit," Sustainability, MDPI, vol. 15(19), pages 1-13, September.
  38. Yingya Zhou & Linwei Ma & Weidou Ni & Colin Yu, 2023. "Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting," Energies, MDPI, vol. 16(5), pages 1-18, February.
  39. Mumin Zhang & Yuzhi Wang & Haochen Zhang & Zhiyun Peng & Junjie Tang, 2023. "A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster," Mathematics, MDPI, vol. 11(3), pages 1-17, January.
  40. Md. Ahasan Habib & M. J. Hossain, 2024. "Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering," Energies, MDPI, vol. 17(5), pages 1-23, March.
  41. Xiaomei Wu & Songjun Jiang & Chun Sing Lai & Zhuoli Zhao & Loi Lei Lai, 2022. "Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network," Energies, MDPI, vol. 15(18), pages 1-16, September.
  42. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
  43. 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).
  44. Christopher Jung, 2024. "Recent Development and Future Perspective of Wind Power Generation," Energies, MDPI, vol. 17(21), pages 1-5, October.
  45. Juan Manuel González Sopeña & Vikram Pakrashi & Bidisha Ghosh, 2022. "A Spiking Neural Network Based Wind Power Forecasting Model for Neuromorphic Devices," Energies, MDPI, vol. 15(19), pages 1-24, October.
  46. Karthik Tamvada & Rohit Babu, 2022. "Control of doubly fed induction generator for power quality improvement: an overview," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2809-2832, December.
  47. 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).
  48. Wu, Zhou & Zeng, Shaoxiong & Jiang, Ruiqi & Zhang, Haoran & Yang, Zhile, 2023. "Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks," Energy, Elsevier, vol. 270(C).
  49. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
  50. Zixiang Yan & Wen Zhou & Jinxiao Li & Xuedan Zhu & Yuxin Zang & Liuyi Zhang, 2024. "Skillful Seasonal Prediction of Global Onshore Wind Resources in SIDRI-ESS V1.0," Sustainability, MDPI, vol. 16(17), pages 1-16, September.
  51. Jiang, Wenjun & Liu, Bo & Liang, Yang & Gao, Huanxiang & Lin, Pengfei & Zhang, Dongqin & Hu, Gang, 2024. "Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables," Applied Energy, Elsevier, vol. 353(PB).
  52. 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).
  53. Fachrizal Aksan & Vishnu Suresh & Przemysław Janik & Tomasz Sikorski, 2023. "Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models," Energies, MDPI, vol. 16(14), pages 1-24, July.
  54. Hou, Guolian & Wang, Junjie & Fan, Yuzhen & Zhang, Jianhua & Huang, Congzhi, 2024. "A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation," Renewable Energy, Elsevier, vol. 226(C).
  55. 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).
  56. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhou, Qingyu & Fan, Hang, 2023. "Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction," Applied Energy, Elsevier, vol. 336(C).
  57. 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).
  58. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
  59. Chamin Geng & Zhuoyue Shi & Xianhao Chen & Ziwen Sun & Yawei Jin & Tian Shi & Xiao Wu, 2024. "Stochastic Capacity Optimization of an Integrated BFGCC–MSHS–Wind–Solar Energy System for the Decarbonization of a Steelmaking Plant," Energies, MDPI, vol. 17(12), pages 1-19, June.
  60. 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).
  61. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
  62. Hamed Safayenikoo & Mohammad Khajehzadeh & Moncef L. Nehdi, 2022. "Novel Evolutionary-Optimized Neural Network for Predicting Fresh Concrete Slump," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
  63. Ghimire, Sujan & Nguyen-Huy, Thong & AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2023. "A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction," Energy, Elsevier, vol. 275(C).
  64. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
  65. Wang, Yun & Chen, Tuo & Zou, Runmin & Song, Dongran & Zhang, Fan & Zhang, Lingjun, 2022. "Ensemble probabilistic wind power forecasting with multi-scale features," Renewable Energy, Elsevier, vol. 201(P1), pages 734-751.
  66. Hu, Huanling & Wang, Lin & Zhang, Dabin & Ling, Liwen, 2023. "Rolling decomposition method in fusion with echo state network for wind speed forecasting," Renewable Energy, Elsevier, vol. 216(C).
  67. Xiaohan Huang & Aihua Jiang, 2022. "Wind Power Generation Forecast Based on Multi-Step Informer Network," Energies, MDPI, vol. 15(18), pages 1-17, September.
  68. Shin, Heesoo & Rüttgers, Mario & Lee, Sangseung, 2023. "Effects of spatiotemporal correlations in wind data on neural network-based wind predictions," Energy, Elsevier, vol. 279(C).
  69. Duan, Jikai & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Zuo, Hongchao & Bai, Yulong & Chen, Bolong, 2022. "A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error," Renewable Energy, Elsevier, vol. 200(C), pages 788-808.
  70. Wen, Songkang & Li, Yanting & Su, Yan, 2022. "A new hybrid model for power forecasting of a wind farm using spatial–temporal correlations," Renewable Energy, Elsevier, vol. 198(C), pages 155-168.
  71. Heng, Jiani & Hong, Yongmiao & Hu, Jianming & Wang, Shouyang, 2022. "Probabilistic and deterministic wind speed forecasting based on non-parametric approaches and wind characteristics information," Applied Energy, Elsevier, vol. 306(PA).
  72. Hou, Guolian & Wang, Junjie & Fan, Yuzhen, 2024. "Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction," Energy, Elsevier, vol. 286(C).
  73. Linlin Yu & Jiafeng Wu & Yuming Cheng & Gaojun Meng & Shuyu Chen & Yang Lu & Ke Xu, 2024. "Control Strategy for Wind Farms-Energy Storage Participation in Primary Frequency Regulation Considering Wind Turbine Operation State," Energies, MDPI, vol. 17(14), pages 1-13, July.
  74. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  75. Peláez-Rodríguez, C. & Pérez-Aracil, J. & Fister, D. & Prieto-Godino, L. & Deo, R.C. & Salcedo-Sanz, S., 2022. "A hierarchical classification/regression algorithm for improving extreme wind speed events prediction," Renewable Energy, Elsevier, vol. 201(P2), pages 157-178.
  76. Parri, Srihari & Teeparthi, Kiran & Kosana, Vishalteja, 2023. "A hybrid VMD based contextual feature representation approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 219(P1).
  77. Lai, Chunyang & Kazemtabrizi, Behzad, 2024. "A novel data-driven tighten-constraint method for wind-hydro hybrid power system to improve day-ahead plan performance in real-time operation," Applied Energy, Elsevier, vol. 371(C).
  78. Zhang, Guowei & Zhang, Yi & Wang, Hui & Liu, Da & Cheng, Runkun & Yang, Di, 2024. "Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network," Energy, Elsevier, vol. 288(C).
  79. Kim, Daeyoung & Ryu, Geonhwa & Moon, Chaejoo & Kim, Bumsuk, 2024. "Accuracy of a short-term wind power forecasting model based on deep learning using LiDAR-SCADA integration: A case study of the 400-MW Anholt offshore wind farm," Applied Energy, Elsevier, vol. 373(C).
  80. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
  81. Joseph, Lionel P. & Deo, Ravinesh C. & Casillas-Pérez, David & Prasad, Ramendra & Raj, Nawin & Salcedo-Sanz, Sancho, 2024. "Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model," Applied Energy, Elsevier, vol. 359(C).
  82. Ziran Yuan & Pengli Zhang & Bo Ming & Xiaobo Zheng & Lu Tian, 2023. "Joint Forecasting Method of Wind and Solar Outputs Considering Temporal and Spatial Correlation," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
  83. Yang, Mao & Han, Chao & Zhang, Wei & Wang, Bo, 2024. "A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information," Energy, Elsevier, vol. 294(C).
  84. Li, Qingyang & Wang, Guosong & Wu, Xinrong & Gao, Zhigang & Dan, Bo, 2024. "Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN," Energy, Elsevier, vol. 299(C).
  85. Xue Zhou & Yajian Ke & Jianhui Zhu & Weiwei Cui, 2023. "Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting," Sustainability, MDPI, vol. 16(1), pages 1-26, December.
  86. Mounir Alliche & Redha Rebhi & Noureddine Kaid & Younes Menni & Houari Ameur & Mustafa Inc & Hijaz Ahmad & Giulio Lorenzini & Ayman A. Aly & Sayed K. Elagan & Bassem F. Felemban, 2021. "Estimation of the Wind Energy Potential in Various North Algerian Regions," Energies, MDPI, vol. 14(22), pages 1-13, November.
  87. Al-Shetwi, Ali Q. & Atawi, Ibrahem E. & Abuelrub, Ahmad & Hannan, M.A., 2023. "Techno-economic assessment and optimal design of hybrid power generation-based renewable energy systems," Technology in Society, Elsevier, vol. 75(C).
  88. Ladislav Zjavka, 2023. "Solar and Wind Quantity 24 h—Series Prediction Using PDE-Modular Models Gradually Developed according to Spatial Pattern Similarity," Energies, MDPI, vol. 16(3), pages 1-16, January.
  89. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
  90. 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).
  91. Bhagwan, N. & Evans, M., 2023. "A review of industry 4.0 technologies used in the production of energy in China, Germany, and South Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
  92. 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).
  93. Xu, Xuefang & Hu, Shiting & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong & Li, Zhi, 2023. "Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm," Energy, Elsevier, vol. 262(PA).
  94. 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.
  95. Dong, Xiaochong & Sun, Yingyun & Dong, Lei & Li, Jian & Li, Yan & Di, Lei, 2023. "Transferable wind power probabilistic forecasting based on multi-domain adversarial networks," Energy, Elsevier, vol. 285(C).
  96. Yifei Zhang & Kang Li & Li Zhang, 2024. "Hybrid ANPC Grid-Tied Inverter Design with Passivity-Based Sliding Mode Control Strategy," Energies, MDPI, vol. 17(15), pages 1-17, July.
  97. Cui, Bodi & Weng, Yang & Zhang, Ning, 2022. "A feature extraction and machine learning framework for bearing fault diagnosis," Renewable Energy, Elsevier, vol. 191(C), pages 987-997.
  98. Fachrizal Aksan & Yang Li & Vishnu Suresh & Przemysław Janik, 2023. "Multistep Forecasting of Power Flow Based on LSTM Autoencoder: A Study Case in Regional Grid Cluster Proposal," Energies, MDPI, vol. 16(13), pages 1-20, June.
  99. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.
  100. Li, Guangkuo & Chen, Laijun & Xue, Xiaodai & Guo, Zhongjie & Wang, Guohua & Xie, Ningning & Mei, Shengwei, 2022. "Multi-mode optimal operation of advanced adiabatic compressed air energy storage: Explore its value with condenser operation," Energy, Elsevier, vol. 248(C).
  101. He, Xingyue & He, Bitao & Qin, Tao & Lin, Chuan & Yang, Jing, 2024. "Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm," Energy, Elsevier, vol. 305(C).
  102. 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).
  103. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
  104. Avci, Mualla Gonca & Avci, Mustafa & Battarra, Maria & Erdoğan, Güneş, 2024. "The wildfire suppression problem with multiple types of resources," European Journal of Operational Research, Elsevier, vol. 316(2), pages 488-502.
  105. Zhu, Jianhua & He, Yaoyao & Gao, Zhiwei, 2023. "Wind power interval and point prediction model using neural network based multi-objective optimization," Energy, Elsevier, vol. 283(C).
  106. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
  107. Ejigu Tefera Habtemariam & Kula Kekeba & María Martínez-Ballesteros & Francisco Martínez-Álvarez, 2023. "A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia," Energies, MDPI, vol. 16(5), pages 1-22, February.
  108. Zheng, Jianqin & Du, Jian & Wang, Bohong & Klemeš, Jiří Jaromír & Liao, Qi & Liang, Yongtu, 2023. "A hybrid framework for forecasting power generation of multiple renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
  109. Abdulrahman A. Alghamdi & Abdelhameed Ibrahim & El-Sayed M. El-Kenawy & Abdelaziz A. Abdelhamid, 2023. "Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-30, January.
  110. Xu, Bin, 2023. "Exploring the sustainable growth pathway of wind power in China: Using the semiparametric regression model," Energy Policy, Elsevier, vol. 183(C).
  111. Baggio, Roberta & Muzy, Jean-François, 2024. "Improving probabilistic wind speed forecasting using M-Rice distribution and spatial data integration," Applied Energy, Elsevier, vol. 360(C).
  112. 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).
  113. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
  114. Wang, Yun & Xu, Houhua & Song, Mengmeng & Zhang, Fan & Li, Yifen & Zhou, Shengchao & Zhang, Lingjun, 2023. "A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting," Applied Energy, Elsevier, vol. 333(C).
  115. Mirza, Adeel Feroz & Shu, Zhaokun & Usman, Muhammad & Mansoor, Majad & Ling, Qiang, 2024. "Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction," Renewable Energy, Elsevier, vol. 220(C).
  116. Wang, Yufeng & Yang, Zihan & Ma, Jianhua & Jin, Qun, 2024. "A wind speed forecasting framework for multiple turbines based on adaptive gate mechanism enhanced multi-graph attention networks," Applied Energy, Elsevier, vol. 372(C).
  117. Tian, Chaonan & Niu, Tong & Wei, Wei, 2022. "Developing a wind power forecasting system based on deep learning with attention mechanism," Energy, Elsevier, vol. 257(C).
  118. Yu Shi & Fei Lv & Xuefeng Gao & Minglei Jiang & Huan Luo & Ruhang Xu, 2023. "A Bi-Level Optimal Operation Model for Small-Scale Active Distribution Networks Considering the Coupling Fluctuation of Spot Electricity Prices and Renewable Energy Sources," Energies, MDPI, vol. 16(11), pages 1-26, June.
  119. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Yuan, Caichenran & Cang, Junjie & Zhang, Kai & Pecht, Michael, 2024. "Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model," Energy, Elsevier, vol. 298(C).
  120. 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).
  121. López, Germánico & Arboleya, Pablo, 2022. "Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador," Renewable Energy, Elsevier, vol. 183(C), pages 351-368.
  122. Li, Jiale & Song, Zihao & Wang, Xuefei & Wang, Yanru & Jia, Yaya, 2022. "A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD," Energy, Elsevier, vol. 251(C).
  123. Abdulaziz S. Alkabaa & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Nazemi & El Mostafa Kalmoun, 2022. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
  124. Sobolewski, Robert Adam & Tchakorom, Médane & Couturier, Raphaël, 2023. "Gradient boosting-based approach for short- and medium-term wind turbine output power prediction," Renewable Energy, Elsevier, vol. 203(C), pages 142-160.
  125. Jiafei Huan & Li Deng & Yue Zhu & Shangguang Jiang & Fei Qi, 2024. "Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration," Energies, MDPI, vol. 17(10), pages 1-22, May.
  126. Kyriakos Skarlatos & Eleni S. Bekri & Dimitrios Georgakellos & Polychronis Economou & Sotirios Bersimis, 2023. "Projecting Annual Rainfall Timeseries Using Machine Learning Techniques," Energies, MDPI, vol. 16(3), pages 1-20, February.
  127. Wu, Qiang & Zheng, Hongling & Guo, Xiaozhu & Liu, Guangqiang, 2022. "Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks," Renewable Energy, Elsevier, vol. 199(C), pages 977-992.
  128. Kübra Tümay Ateş, 2023. "Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
  129. 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).
  130. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
  131. Guo, Nai-Zhi & Shi, Ke-Zhong & Li, Bo & Qi, Liang-Wen & Wu, Hong-Hui & Zhang, Zi-Liang & Xu, Jian-Zhong, 2022. "A physics-inspired neural network model for short-term wind power prediction considering wake effects," Energy, Elsevier, vol. 261(PA).
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