Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake
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 e...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/162096 |
| _version_ | 1824454509348257792 |
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| author | Purohit, Shantanu Ng, Eddie Yin Kwee Ijaz Fazil Syed Ahmed Kabir |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Purohit, Shantanu Ng, Eddie Yin Kwee Ijaz Fazil Syed Ahmed Kabir |
| author_sort | Purohit, Shantanu |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:23:26Z |
| format | Journal Article |
| id | ntu-10356/162096 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:23:26Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1620962022-10-04T04:45:21Z Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake Purohit, Shantanu Ng, Eddie Yin Kwee Ijaz Fazil Syed Ahmed Kabir School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Wake Velocity Turbulence Intensity 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. 2022-10-04T04:45:21Z 2022-10-04T04:45:21Z 2022 Journal Article 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 0960-1481 https://hdl.handle.net/10356/162096 10.1016/j.renene.2021.11.097 2-s2.0-85120454994 184 405 420 en Renewable Energy © 2021 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Mechanical engineering Wake Velocity Turbulence Intensity Purohit, Shantanu Ng, Eddie Yin Kwee Ijaz Fazil Syed Ahmed Kabir Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake |
| title | Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake |
| title_full | Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake |
| title_fullStr | Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake |
| title_full_unstemmed | Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake |
| title_short | Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake |
| title_sort | evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake |
| topic | Engineering::Mechanical engineering Wake Velocity Turbulence Intensity |
| url | https://hdl.handle.net/10356/162096 |
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