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Artificial Neural Networks based wake model for power prediction of wind farm

Citations

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Cited by:

  1. Moss, Coleman & Maulik, Romit & Iungo, Giacomo Valerio, 2024. "Augmenting insights from wind turbine data through data-driven approaches," Applied Energy, Elsevier, vol. 376(PA).
  2. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
  3. Sun, Haiying & Yang, Hongxing, 2023. "Wind farm layout and hub height optimization with a novel wake model," Applied Energy, Elsevier, vol. 348(C).
  4. Wang, Mingwei & Zhang, Mingming & Qin, Caiyan & Sun, Haiying & Deng, Xiaowei, 2026. "A data-driven double-Gaussian wake model reflecting the wake evolution process," Renewable Energy, Elsevier, vol. 257(C).
  5. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
  6. Pia Leminski & Enzo Pinheiro & Taha B. M. J. Ouarda, 2025. "Ensemble of Artificial Neural Networks for Seasonal Forecasting of Wind Speed in Eastern Canada," Energies, MDPI, vol. 18(11), pages 1-17, June.
  7. Ai, Chunyu & He, Shan & Hu, Heng & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
  8. Bin Shahadat, Muhammad Rubayat & Doranehgard, Mohammad Hossein & Cai, Weibing & Li, Zheng, 2025. "Large eddy simulation of wind farm performance in horizontally and vertically staggered layouts," Energy, Elsevier, vol. 322(C).
  9. Han, Yixiao & Liao, Yanfen & Ma, Xiaoqian & Guo, Xing & Li, Changxin & Liu, Xinyu, 2023. "Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network," Renewable Energy, Elsevier, vol. 215(C).
  10. Dong, Fuxiang & Wang, Zhonghao & Mu, Chunjin & Liu, Jinfu & Yu, Daren & Li, Hong, 2025. "Wide angle range wind direction ultra-short-term interval prediction based on an improved loss function," Energy, Elsevier, vol. 334(C).
  11. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
  12. 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).
  13. 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.
  14. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
  15. Srinivas, Gollapalli Veera Satya & Rajesh, M.V., 2026. "Spatial-temporal modeling with DL-based hybrid cheetah hippopotamus optimizer framework for wind energy forecasting and turbine performance enhancement," Renewable Energy, Elsevier, vol. 256(PA).
  16. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
  17. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
  18. Ahmadi, Mehdi & Knorr, Lukas & Meschede, Henning, 2025. "Improvement of wind power utilization through flexible operation of data center in wind parks," Renewable Energy, Elsevier, vol. 248(C).
  19. Yang, Kun & Zhang, Mingming & Yang, Shanghui & Song, Yuwei & Dong, Xinhui & Deng, Yanfei & Deng, Xiaowei, 2025. "Pareto frontier for multi-objective wind farm layout optimization balancing power production and turbine fatigue life," Renewable Energy, Elsevier, vol. 252(C).
  20. Yang, Shanghui & Deng, Xiaowei & Li, Qinglan, 2025. "A joint optimization framework for power and fatigue life based on cooperative wake steering of wind farm," Energy, Elsevier, vol. 319(C).
  21. Nguyen, Thuy-Hai & Toubeau, Jean-François & Jaeger, Emmanuel De & Vallée, François, 2026. "Topology-aware surrogate for future offshore wind farms using machine learning," Renewable Energy, Elsevier, vol. 256(PA).
  22. 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.
  23. Luo, Zhaohui & Wang, Longyan & Xu, Jian & Wang, Zilu & Yuan, Jianping & Tan, Andy C.C., 2024. "A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements," Energy, Elsevier, vol. 294(C).
  24. Yang, Shanghui & Deng, Xiaowei & Ti, Zilong & Yan, Bowen & Yang, Qingshan, 2022. "Cooperative yaw control of wind farm using a double-layer machine learning framework," Renewable Energy, Elsevier, vol. 193(C), pages 519-537.
  25. Mian, H.H. & Machot, F.A. & Ullah, H. & Keprate, A. & Siddiqui, M.S., 2025. "Advances in computational intelligence for floating offshore wind turbines aerodynamics: Current state review and future potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
  26. Li, Peiyi & Che, Yanbo & Hua, Anran & Wang, Lei & Zheng, Mengxiang & Guo, Xiaojiang, 2025. "A data-physics hybrid-driven layout optimization framework for large-scale wind farms," Applied Energy, Elsevier, vol. 392(C).
  27. Hoang, Kiet Tuan & Boersma, Sjoerd & Mesbah, Ali & Imsland, Lars Struen, 2025. "Multi-objective Bayesian optimisation over sparse subspaces for model predictive control of wind farms," Renewable Energy, Elsevier, vol. 247(C).
  28. Kabir Bashir Shariff & Sylvain S. Guillou, 2025. "Towards a Generalized Tidal Turbine Wake Analytical Model for Turbine Placement in Array Accounting for Added Turbulence," Energies, MDPI, vol. 18(9), pages 1-27, April.
  29. Yang Shen & Jinkui Zhu & Peng Hou & Shuowang Zhang & Xinglin Wang & Guodong He & Chao Lu & Enyu Wang & Yiwen Wu, 2025. "A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms," Energies, MDPI, vol. 18(13), pages 1-16, June.
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