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Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning

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  • Richmond, M.
  • Sobey, A.
  • Pandit, R.
  • Kolios, A.

Abstract

Wind turbine flow field prediction is difficult as it requires computationally expensive computational fluid dynamics (CFD) models. The contribution of this paper is to propose and develop a method for stochastic analysis of an offshore wind farm using CFD and a non-intrusive stochastic expansion. The approach is developed through testing a range of machine-learning methods, evaluating dataset requirements and comparing the accuracy against site measurement data. The approach used is detailed and the results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation models compared are Artificial Neural Networks, Gaussian Process, Radial Basis Function, Random Forest and Support Vector Regression. RBF achieves a mean absolute error relative to the CFD model of only 0.54% and the error of the SVR predictions relative to the real data, with scatter, was 12%, compared to 16% from Jensen. This approach has the potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.

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  • Richmond, M. & Sobey, A. & Pandit, R. & Kolios, A., 2020. "Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning," Renewable Energy, Elsevier, vol. 161(C), pages 650-661.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:650-661
    DOI: 10.1016/j.renene.2020.07.083
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Shanhong & Yu, Guanghui & Guo, Yu & Wang, Yang, 2023. "Modelling development and optimization on hydrodynamics and energy utilization of fish culture tank based on computational fluid dynamics and machine learning," Energy, Elsevier, vol. 276(C).
    3. 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).
    4. Sayed Abdul Majid Gilani & Abigail Copiaco & Liza Gernal & Naveed Yasin & Gayatri Nair & Imran Anwar, 2023. "Savior or Distraction for Survival: Examining the Applicability of Machine Learning for Rural Family Farms in the United Arab Emirates," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    5. Antonio Lorenzo-Espejo & Alejandro Escudero-Santana & María-Luisa Muñoz-Díaz & Alicia Robles-Velasco, 2022. "Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    6. Clara Matutano Molina & Christian Velasco-Gallego & Nerea Portillo-Juan & Vicente Negro Valdecantos & Nieves Cubo-Mateo, 2023. "Geospatial Analysis of Scour in Offshore Wind Farms," Energies, MDPI, vol. 16(15), pages 1-21, July.

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