A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation
This study investigates the predictive modeling of temperature (T(K)) using a dataset of over 128,000 data points characterized by x, y, and z coordinates as inputs. The case study considered here is a photovoltaic system with porous collector for enhancing the efficiency of solar system. Computatio...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25002588 |
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| author | Yinling Wang Lei Yu Mazhar Ali Imran Ali Khan Tahir Maqsood Haining Gao Qi Wang Xiaolei Guo |
| author_facet | Yinling Wang Lei Yu Mazhar Ali Imran Ali Khan Tahir Maqsood Haining Gao Qi Wang Xiaolei Guo |
| author_sort | Yinling Wang |
| collection | DOAJ |
| description | This study investigates the predictive modeling of temperature (T(K)) using a dataset of over 128,000 data points characterized by x, y, and z coordinates as inputs. The case study considered here is a photovoltaic system with porous collector for enhancing the efficiency of solar system. Computational modeling was carried out via CFD (Computational Fluid Dynamics), and the temperature distribution was determined which was later used in machine learning (ML) evaluation. Indeed, a hybrid model was developed combining CFD and ML for the first time to predict temperature distribution versus special coordinates in a photovoltaic thermal system. Three advanced machine learning models, i.e., Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Histogram-based Gradient Boosting (HGB) were applied to analyze and predict T in system. A systematic preprocessing pipeline was developed to enhance model performance, including outlier detection and feature normalization. Hyperparameter optimization process in this study uses the Water Cycle Algorithm (WCA), a metaheuristic method inspired by natural processes. Among the models, XGB emerged as the best performer, revealing a total R2 of 0.99823, a Root Mean Square Error (RMSE) of 0.06596, and a Mean Absolute Error (MAE) of 0.04442. These results demonstrated the capability of machine learning to accurately capture complex relationships within structured datasets. |
| format | Article |
| id | doaj-art-ba36060517bf45648095daf4e092894f |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-ba36060517bf45648095daf4e092894f2025-08-20T02:17:28ZengElsevierCase Studies in Thermal Engineering2214-157X2025-05-016910599810.1016/j.csite.2025.105998A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validationYinling Wang0Lei Yu1Mazhar Ali2Imran Ali Khan3Tahir Maqsood4Haining Gao5Qi Wang6Xiaolei Guo7International Joint Laboratory of New Energy Digitalization Technology in Henan Province, Huanghuai University, Zhumadian, Henan, 463000, China; Corresponding author.International Joint Laboratory of New Energy Digitalization Technology in Henan Province, Huanghuai University, Zhumadian, Henan, 463000, China; Department of Computer Science, COMSATS University Islamabad, Abbottabad, 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad, 22060, Pakistan; Corresponding author.Department of Computer Science, COMSATS University Islamabad, Abbottabad, 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad, 22060, PakistanInternational Joint Laboratory of New Energy Digitalization Technology in Henan Province, Huanghuai University, Zhumadian, Henan, 463000, ChinaInternational Joint Laboratory of New Energy Digitalization Technology in Henan Province, Huanghuai University, Zhumadian, Henan, 463000, ChinaInternational Joint Laboratory of New Energy Digitalization Technology in Henan Province, Huanghuai University, Zhumadian, Henan, 463000, ChinaThis study investigates the predictive modeling of temperature (T(K)) using a dataset of over 128,000 data points characterized by x, y, and z coordinates as inputs. The case study considered here is a photovoltaic system with porous collector for enhancing the efficiency of solar system. Computational modeling was carried out via CFD (Computational Fluid Dynamics), and the temperature distribution was determined which was later used in machine learning (ML) evaluation. Indeed, a hybrid model was developed combining CFD and ML for the first time to predict temperature distribution versus special coordinates in a photovoltaic thermal system. Three advanced machine learning models, i.e., Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Histogram-based Gradient Boosting (HGB) were applied to analyze and predict T in system. A systematic preprocessing pipeline was developed to enhance model performance, including outlier detection and feature normalization. Hyperparameter optimization process in this study uses the Water Cycle Algorithm (WCA), a metaheuristic method inspired by natural processes. Among the models, XGB emerged as the best performer, revealing a total R2 of 0.99823, a Root Mean Square Error (RMSE) of 0.06596, and a Mean Absolute Error (MAE) of 0.04442. These results demonstrated the capability of machine learning to accurately capture complex relationships within structured datasets.http://www.sciencedirect.com/science/article/pii/S2214157X25002588CFDHybrid modelingSolar energyExtreme gradient boostingHistogram-based gradient boostingWater cycle algorithm |
| spellingShingle | Yinling Wang Lei Yu Mazhar Ali Imran Ali Khan Tahir Maqsood Haining Gao Qi Wang Xiaolei Guo A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation Case Studies in Thermal Engineering CFD Hybrid modeling Solar energy Extreme gradient boosting Histogram-based gradient boosting Water cycle algorithm |
| title | A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation |
| title_full | A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation |
| title_fullStr | A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation |
| title_full_unstemmed | A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation |
| title_short | A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation |
| title_sort | hybrid cfd and machine learning study of energy performance of photovoltaic systems with a porous collector model development and validation |
| topic | CFD Hybrid modeling Solar energy Extreme gradient boosting Histogram-based gradient boosting Water cycle algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25002588 |
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