Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions
Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machin...
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Elsevier Ltd
2024
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| Online Access: | http://irep.iium.edu.my/112174/2/112174_Advancing%20solar%20PV%20panel%20power%20prediction.pdf http://irep.iium.edu.my/112174/8/112174_Advancing%20solar%20PV%20panel%20power%20prediction_SCOPUS.pdf http://irep.iium.edu.my/112174/ https://www.sciencedirect.com/science/article/pii/S2214157X24004908/pdfft?md5=7abfe3686998480f7d3783fe557a5b8f&pid=1-s2.0-S2214157X24004908-main.pdf https://doi.org/10.1016/j.csite.2024.104459 |
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my.iium.irep.1121742024-09-20T07:30:44Z http://irep.iium.edu.my/112174/ Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions Tripathi, Abhishek Kumar Mangalpady, Aruna Elumalai, P.V. Karthik, Krishnasamy Khan, Sher Afghan Asif, Mohammad Rao, Koppula Srinivas TJ163.26 Energy conservation Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R-value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R of 0.88, an MSE of 0.49, and an MAE of 0.63. This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation. Elsevier Ltd 2024-05-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/112174/2/112174_Advancing%20solar%20PV%20panel%20power%20prediction.pdf application/pdf en http://irep.iium.edu.my/112174/8/112174_Advancing%20solar%20PV%20panel%20power%20prediction_SCOPUS.pdf Tripathi, Abhishek Kumar and Mangalpady, Aruna and Elumalai, P.V. and Karthik, Krishnasamy and Khan, Sher Afghan and Asif, Mohammad and Rao, Koppula Srinivas (2024) Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions. Case Studies in Thermal Engineering, 59. pp. 1-15. ISSN 2214-157X https://www.sciencedirect.com/science/article/pii/S2214157X24004908/pdfft?md5=7abfe3686998480f7d3783fe557a5b8f&pid=1-s2.0-S2214157X24004908-main.pdf https://doi.org/10.1016/j.csite.2024.104459 |
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TJ163.26 Energy conservation |
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TJ163.26 Energy conservation Tripathi, Abhishek Kumar Mangalpady, Aruna Elumalai, P.V. Karthik, Krishnasamy Khan, Sher Afghan Asif, Mohammad Rao, Koppula Srinivas Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions |
| description |
Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R-value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R of 0.88, an MSE of 0.49, and an MAE of 0.63. This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation. |
| format |
Article |
| author |
Tripathi, Abhishek Kumar Mangalpady, Aruna Elumalai, P.V. Karthik, Krishnasamy Khan, Sher Afghan Asif, Mohammad Rao, Koppula Srinivas |
| author_facet |
Tripathi, Abhishek Kumar Mangalpady, Aruna Elumalai, P.V. Karthik, Krishnasamy Khan, Sher Afghan Asif, Mohammad Rao, Koppula Srinivas |
| author_sort |
Tripathi, Abhishek Kumar |
| title |
Advancing solar PV panel power prediction: A comparative
machine learning approach in fluctuating environmental
conditions |
| title_short |
Advancing solar PV panel power prediction: A comparative
machine learning approach in fluctuating environmental
conditions |
| title_full |
Advancing solar PV panel power prediction: A comparative
machine learning approach in fluctuating environmental
conditions |
| title_fullStr |
Advancing solar PV panel power prediction: A comparative
machine learning approach in fluctuating environmental
conditions |
| title_full_unstemmed |
Advancing solar PV panel power prediction: A comparative
machine learning approach in fluctuating environmental
conditions |
| title_sort |
advancing solar pv panel power prediction: a comparative
machine learning approach in fluctuating environmental
conditions |
| publisher |
Elsevier Ltd |
| publishDate |
2024 |
| url |
http://irep.iium.edu.my/112174/2/112174_Advancing%20solar%20PV%20panel%20power%20prediction.pdf http://irep.iium.edu.my/112174/8/112174_Advancing%20solar%20PV%20panel%20power%20prediction_SCOPUS.pdf http://irep.iium.edu.my/112174/ https://www.sciencedirect.com/science/article/pii/S2214157X24004908/pdfft?md5=7abfe3686998480f7d3783fe557a5b8f&pid=1-s2.0-S2214157X24004908-main.pdf https://doi.org/10.1016/j.csite.2024.104459 |
| _version_ |
1811679603171786752 |
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13.211869 |