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Data-driven modeling of wind farm wake flow based on multi-scale feature recognition

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  • Xu, Dong
  • Li, Zhaobin
  • Yang, Xiaolei
  • Hou, Peng
  • Carmo, Bruno
  • Mao, Xuerui

Abstract

Accurate and efficient predictions of wind flow developments with wake effects accounted are crucial for wind farm layouts and power forecasting. Existing methods can be broadly classified as physical measurement, numerical simulations, physics-based modeling, and data-driven modeling. The first two is of high cost in terms of time and resources, the third suffers from low accuracy due to limited physics modeled, while the last one takes advantage of the large amount of high-quality data available and has become increasingly popular. This study proposes a rapid data-driven modeling method for wind farm wake flow, inspired by video frame interpolation and based on the principle of similarity, which utilizes a multi-scale feature recognition technique. The method transforms wind farm field data into images and predicts wake flow by identifying, matching, and interpolating features from a limited set of wake flow images using the Scale-Invariant Feature Transform (SIFT) and Dynamic Time Warping (DTW) approaches. To demonstrate the effectiveness of the proposed method, six representative cases were evaluated, encompassing mini wind farms with varying turbine spacings, different turbine sizes, combinations of spacing and size variations, different numbers of turbines, and various degrees of wind direction misalignment. A Mean Absolute Percentage Error (MAPE) ranging from 0.68% to 2.28% is achieved. Due to its ability to flexibly compute both 2D and 3D wake flow fields, the proposed method offers unique computational efficiency advantages over Large Eddy Simulation (LES) and Meteodyn WT in scenarios where two-dimensional wake flow fields are sufficient to meet industrial requirements. Therefore, this method can be employed for the extension of the wake flow database serving wind farm design, power prediction, etc., as an alternative to measurements, numerical simulation, and physics-based modeling, balancing efficiency and accuracy.

Suggested Citation

  • Xu, Dong & Li, Zhaobin & Yang, Xiaolei & Hou, Peng & Carmo, Bruno & Mao, Xuerui, 2026. "Data-driven modeling of wind farm wake flow based on multi-scale feature recognition," Renewable Energy, Elsevier, vol. 256(PH).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:ph:s0960148125021810
    DOI: 10.1016/j.renene.2025.124517
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    References listed on IDEAS

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    1. Zhong, Hongmin & Du, Pingan & Tang, Fangning & Wang, Li, 2015. "Lagrangian dynamic large-eddy simulation of wind turbine near wakes combined with an actuator line method," Applied Energy, Elsevier, vol. 144(C), pages 224-233.
    2. Xin Zhang & Haifeng Wang & Bo Wu & Quan Zhou & Youmin Hu, 2023. "A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2449-2462, June.
    3. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    4. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
    5. Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
    6. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    7. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    8. Chen, Zhenyu & Lin, Zhongwei & Zhai, Xiaoya & Liu, Jizhen, 2022. "Dynamic wind turbine wake reconstruction: A Koopman-linear flow estimator," Energy, Elsevier, vol. 238(PB).
    9. Castellani, Francesco & Vignaroli, Andrea, 2013. "An application of the actuator disc model for wind turbine wakes calculations," Applied Energy, Elsevier, vol. 101(C), pages 432-440.
    10. Gao, Xiaoxia & Li, Bingbing & Wang, Tengyuan & Sun, Haiying & Yang, Hongxing & Li, Yonghua & Wang, Yu & Zhao, Fei, 2020. "Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements," Applied Energy, Elsevier, vol. 260(C).
    11. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    12. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
    13. Reddy, Sohail R., 2020. "Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization," Applied Energy, Elsevier, vol. 269(C).
    14. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
    Full references (including those not matched with items on IDEAS)

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