Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models
The incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not undergone exhaustive scrutiny, and there are no pote...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/12/1/153 |
| _version_ | 1797343291136540672 |
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| author | Sunny Kumar Poguluri Yoon Hyeok Bae |
| author_facet | Sunny Kumar Poguluri Yoon Hyeok Bae |
| author_sort | Sunny Kumar Poguluri |
| collection | DOAJ |
| description | The incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not undergone exhaustive scrutiny, and there are no potential or concurrent models for improving the performance of wave energy converter (WEC) devices. This study employs supervised regression ML models, including multi-layer perceptron, support vector regression, and XGBoost, to optimize the geometric aspects of an asymmetric WEC inspired by Salter’s duck, based on key parameters. These important parameters, the ballast weight and its position, vary along a guided line within the available geometric resilience of the asymmetric WEC. Each supervised regression ML model was fine-tuned through hyperparameter optimization using Grid cross-validation. When evaluating the performance of each ML model, it became evident that the tuned hyperparameters of XGBoost led to predictions that strongly aligned with the actual values compared to other models. Furthermore, the study extended to assess the performance of the optimized WEC at the designated deployment test site location. |
| first_indexed | 2024-03-08T10:45:34Z |
| format | Article |
| id | doaj.art-b127af8492934c3e9856f58f9b2dd1c6 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-08T10:45:34Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-b127af8492934c3e9856f58f9b2dd1c62024-01-26T17:17:19ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-01-0112115310.3390/jmse12010153Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning ModelsSunny Kumar Poguluri0Yoon Hyeok Bae1Department of Mechanical and System Design Engineering, Hongik University, Seoul 04066, Republic of KoreaDepartment of Mechanical and System Design Engineering, Hongik University, Seoul 04066, Republic of KoreaThe incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not undergone exhaustive scrutiny, and there are no potential or concurrent models for improving the performance of wave energy converter (WEC) devices. This study employs supervised regression ML models, including multi-layer perceptron, support vector regression, and XGBoost, to optimize the geometric aspects of an asymmetric WEC inspired by Salter’s duck, based on key parameters. These important parameters, the ballast weight and its position, vary along a guided line within the available geometric resilience of the asymmetric WEC. Each supervised regression ML model was fine-tuned through hyperparameter optimization using Grid cross-validation. When evaluating the performance of each ML model, it became evident that the tuned hyperparameters of XGBoost led to predictions that strongly aligned with the actual values compared to other models. Furthermore, the study extended to assess the performance of the optimized WEC at the designated deployment test site location.https://www.mdpi.com/2077-1312/12/1/153asymmetric WECsupervised regression ML modelsdesign optimizationextracted power |
| spellingShingle | Sunny Kumar Poguluri Yoon Hyeok Bae Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models Journal of Marine Science and Engineering asymmetric WEC supervised regression ML models design optimization extracted power |
| title | Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models |
| title_full | Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models |
| title_fullStr | Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models |
| title_full_unstemmed | Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models |
| title_short | Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models |
| title_sort | enhancing wave energy conversion efficiency through supervised regression machine learning models |
| topic | asymmetric WEC supervised regression ML models design optimization extracted power |
| url | https://www.mdpi.com/2077-1312/12/1/153 |
| work_keys_str_mv | AT sunnykumarpoguluri enhancingwaveenergyconversionefficiencythroughsupervisedregressionmachinelearningmodels AT yoonhyeokbae enhancingwaveenergyconversionefficiencythroughsupervisedregressionmachinelearningmodels |