Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162096
Title: Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake
Authors: Purohit, Shantanu
Ng, Eddie Yin Kwee
Ijaz Fazil Syed Ahmed Kabir
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Purohit, S., Ng, E. Y. K. & Ijaz Fazil Syed Ahmed Kabir (2022). Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake. Renewable Energy, 184, 405-420. https://dx.doi.org/10.1016/j.renene.2021.11.097
Journal: Renewable Energy
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.
URI: https://hdl.handle.net/10356/162096
ISSN: 0960-1481
DOI: 10.1016/j.renene.2021.11.097
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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