Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake

Shantanu Purohit, E.Y.K. Ng and Ijaz Fazil Syed Ahmed Kabir

Renewable Energy, 2022, vol. 184, issue C, 405-420

Abstract: In this paper, three machine learning (ML) algorithms, Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGBoost), are validated to estimate the velocity and turbulence intensity of a wind turbine's wake at distinct downstream distances. To this end, a series of high-fidelity numerical simulations for the NREL Phase VI wind turbine is carried out to generate training and test datasets for the three machine learning algorithms. The predicted wake velocity and turbulence intensity from the ML models are also contrasted with significant existing analytical wake models. Machine learning algorithms estimate velocity and turbulence intensity in the wake in a way commensurate to the Computational Fluid Dynamics (CFD) simulations while running at a similar pace as low-fidelity wake models. The results demonstrate that machine learning-based algorithms can predict velocity and turbulence intensity better with higher precision than the traditional analytical wake models.

Keywords: Wake velocity; Turbulence intensity; Support vector regression (SVR); Artificial neural networks (ANN); eXtreme gradient boosting (XGBoost) (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:184:y:2022:i:c:p:405-420

DOI: 10.1016/j.renene.2021.11.097

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