Deep regression analysis for enhanced thermal control in photovoltaic energy systems

• Published in: Scientific reports 2024-12, Vol.14 (1), p.30600
• Main Authors: Elmessery, Wael M., Habib, Abadeer, Shams, Mahmoud Y., Abd El-Hafeez, Tarek, El-Messery, Tamer M., Elsayed, Salah, Fodah, Ahmed E. M., Abdelwahab, Taha A. M., Ali, Khaled A. M., Osman, Yasser K. O. T., Abdelshafie, Mohamed F., El-wahhab, Gomaa G. Abd, Elwakeel, Abdallah E.
• Format: Article
• Language: English
• Subjects: 639/4077; 639/4077/4072; 639/4077/4073; 639/4077/4079; 639/4077/4107; 639/4077/909; 639/705/1046; 639/705/117; 639/705/258; Cooling; Cooling systems; Cost benefit analysis; Cost control; Deep learning; Efficiency; Energy output; Heat detection; Humanities and Social Sciences; multidisciplinary; Neural networks; Photovoltaics; Prediction models; Regression analysis; Renewable energy technologies; Renewable resources; Science; Science (multidisciplinary); Sensitivity analysis; Solar energy; Solar panels
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-81101-x

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 datas