Deep regression analysis for enhanced thermal control in photovoltaic energy systems
Abstract Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system perform...
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| Format: | Article |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81101-x |
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| author | Wael M. Elmessery Abadeer Habib Mahmoud Y. Shams Tarek Abd El-Hafeez Tamer M. El-Messery Salah Elsayed Ahmed E. M. Fodah Taha A. M. Abdelwahab Khaled A. M. Ali Yasser K. O. T. Osman Mohamed F. Abdelshafie Gomaa G. Abd El-wahhab Abdallah E. Elwakeel |
| author_facet | Wael M. Elmessery Abadeer Habib Mahmoud Y. Shams Tarek Abd El-Hafeez Tamer M. El-Messery Salah Elsayed Ahmed E. M. Fodah Taha A. M. Abdelwahab Khaled A. M. Ali Yasser K. O. T. Osman Mohamed F. Abdelshafie Gomaa G. Abd El-wahhab Abdallah E. Elwakeel |
| author_sort | Wael M. Elmessery |
| collection | DOAJ |
| description | Abstract Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system performance. Existing methods for quantifying cooling efficiency lack precision, hindering the optimization of PV system maintenance and renewable energy output. This research introduces a novel approach utilizing deep learning techniques to address these limitations. A U-Net architecture is employed to segment solar panels from background elements in thermal imaging videos, facilitating a comprehensive analysis of cooling system efficiency. Two predictive models—a 3-layer Feedforward Neural Network (FNN) and a proposed Convolutional Neural Network (CNN)—are developed and compared for estimating cooling percentages from individual images. The study aims to enhance the precision and reliability of heat mapping capabilities for non-invasive, vision-based monitoring of photovoltaic cooling dynamics. By leveraging deep regression techniques, the proposed CNN model demonstrates superior predictive capability compared to traditional methods, enabling accurate estimation of cooling efficiencies across diverse scenarios. Experimental evaluation illustrates the supremacy of the CNN model in predictive capability, yielding a mean square error (MSE) of just 0.001171821, as opposed to the FNN’s MSE of 0.016. Furthermore, the CNN demonstrates remarkable improvements in mean absolute error (MAE) and R-square, registering values of 1.2% and 0.95, respectively, whereas the FNN posts comparatively inferior numbers of 3.5% and 0.85. This research introduces labeled thermal imaging datasets and tailored deep learning architectures, accelerating advancements in renewable energy technology solutions. Moreover, the study provides insights into the practical implementation and cost-effectiveness of the proposed cooling efficiency monitoring system, highlighting hardware requirements, integration with existing infrastructure, and sensitivity analysis. The economic viability and scalability of the system are assessed through comprehensive cost-benefit analysis and scalability assessment, demonstrating significant potential for cost savings and revenue increases in large-scale PV installations. Furthermore, strategies for addressing limitations, enhancing predictive accuracy, and scaling to larger datasets are discussed, laying the groundwork for future research and industry collaboration in the field of photovoltaic thermal management optimization. |
| format | Article |
| id | doaj-art-963097a35f414a07a761b9ddc02faf94 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-963097a35f414a07a761b9ddc02faf942024-12-29T12:18:55ZengNature PortfolioScientific Reports2045-23222024-12-0114112210.1038/s41598-024-81101-xDeep regression analysis for enhanced thermal control in photovoltaic energy systemsWael M. Elmessery0Abadeer Habib1Mahmoud Y. Shams2Tarek Abd El-Hafeez3Tamer M. El-Messery4Salah Elsayed5Ahmed E. M. Fodah6Taha A. M. Abdelwahab7Khaled A. M. Ali8Yasser K. O. T. Osman9Mohamed F. Abdelshafie10Gomaa G. Abd El-wahhab11Abdallah E. Elwakeel12Agricultural Engineering Department, Faculty of Agriculture, Kafrelsheikh UniversityInternational Research Centre “Biotechnologies of the Third Millennium”, Faculty of Biotechnologies (BioTech), ITMO UniversityDepartment of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheikh UniversityDepartment of Computer Science, Faculty of Science, Minia UniversityInternational Research Centre “Biotechnologies of the Third Millennium”, Faculty of Biotechnologies (BioTech), ITMO UniversityAgricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat CityCollege of Agricultural Engineering, Al-Azhar UniversityCollege of Agricultural Engineering, Al-Azhar UniversityCollege of Agricultural Engineering, Al-Azhar UniversityCollege of Agricultural Engineering, Al-Azhar UniversityCollege of Agricultural Engineering, Al-Azhar UniversityCollege of Agricultural Engineering, Al-Azhar UniversityAgricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan UniversityAbstract Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system performance. Existing methods for quantifying cooling efficiency lack precision, hindering the optimization of PV system maintenance and renewable energy output. This research introduces a novel approach utilizing deep learning techniques to address these limitations. A U-Net architecture is employed to segment solar panels from background elements in thermal imaging videos, facilitating a comprehensive analysis of cooling system efficiency. Two predictive models—a 3-layer Feedforward Neural Network (FNN) and a proposed Convolutional Neural Network (CNN)—are developed and compared for estimating cooling percentages from individual images. The study aims to enhance the precision and reliability of heat mapping capabilities for non-invasive, vision-based monitoring of photovoltaic cooling dynamics. By leveraging deep regression techniques, the proposed CNN model demonstrates superior predictive capability compared to traditional methods, enabling accurate estimation of cooling efficiencies across diverse scenarios. Experimental evaluation illustrates the supremacy of the CNN model in predictive capability, yielding a mean square error (MSE) of just 0.001171821, as opposed to the FNN’s MSE of 0.016. Furthermore, the CNN demonstrates remarkable improvements in mean absolute error (MAE) and R-square, registering values of 1.2% and 0.95, respectively, whereas the FNN posts comparatively inferior numbers of 3.5% and 0.85. This research introduces labeled thermal imaging datasets and tailored deep learning architectures, accelerating advancements in renewable energy technology solutions. Moreover, the study provides insights into the practical implementation and cost-effectiveness of the proposed cooling efficiency monitoring system, highlighting hardware requirements, integration with existing infrastructure, and sensitivity analysis. The economic viability and scalability of the system are assessed through comprehensive cost-benefit analysis and scalability assessment, demonstrating significant potential for cost savings and revenue increases in large-scale PV installations. Furthermore, strategies for addressing limitations, enhancing predictive accuracy, and scaling to larger datasets are discussed, laying the groundwork for future research and industry collaboration in the field of photovoltaic thermal management optimization.https://doi.org/10.1038/s41598-024-81101-xPhotovoltaic solar panelsCooling systems monitoringThermal imagingDeep learningImage segmentationComputer vision |
| spellingShingle | Wael M. Elmessery Abadeer Habib Mahmoud Y. Shams Tarek Abd El-Hafeez Tamer M. El-Messery Salah Elsayed Ahmed E. M. Fodah Taha A. M. Abdelwahab Khaled A. M. Ali Yasser K. O. T. Osman Mohamed F. Abdelshafie Gomaa G. Abd El-wahhab Abdallah E. Elwakeel Deep regression analysis for enhanced thermal control in photovoltaic energy systems Scientific Reports Photovoltaic solar panels Cooling systems monitoring Thermal imaging Deep learning Image segmentation Computer vision |
| title | Deep regression analysis for enhanced thermal control in photovoltaic energy systems |
| title_full | Deep regression analysis for enhanced thermal control in photovoltaic energy systems |
| title_fullStr | Deep regression analysis for enhanced thermal control in photovoltaic energy systems |
| title_full_unstemmed | Deep regression analysis for enhanced thermal control in photovoltaic energy systems |
| title_short | Deep regression analysis for enhanced thermal control in photovoltaic energy systems |
| title_sort | deep regression analysis for enhanced thermal control in photovoltaic energy systems |
| topic | Photovoltaic solar panels Cooling systems monitoring Thermal imaging Deep learning Image segmentation Computer vision |
| url | https://doi.org/10.1038/s41598-024-81101-x |
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