Predictive Modeling of Solar PV Panel Operating Temperature over Water Bodies: Comparative Performance Analysis with Ground-Mounted Installations

• Published in: Energies (Basel) 2024-07, Vol.17 (14), p.3489
• Main Authors: Agrawal, Karmendra Kumar, Jha, Shibani Khanra, Mittal, Ravi Kant, Singh, Ajit Pratap, Vashishtha, Sanjay, Gupta, Saurabh, Soni, Manoj Kumar
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Analysis; Efficiency; exponential module temperature model; floating solar PV; ground-mounted solar PV; Humidity; Installation; Mathematical models; Radiation; Regression analysis; Solar energy; Solar energy industry; temperature model; Thermal energy; Velocity; Water temperature; Weather forecasting
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17143489

Solar panel efficiency is significantly influenced by its operating temperature. Recent advancements in emerging renewable energy alternatives have enabled photovoltaic (PV) module installation over water bodies, leveraging their increased efficiency and associated benefits. This paper examines the operational performance of solar panels placed over water bodies, comparing them to ground-mounted solar PV installations. Regression models for panel temperature are developed based on experimental setups at BITS Pilani, India. Developed regression models, including linear, quadratic, and exponential, are utilized to predict the operating temperature of solar PV installations above water bodies. These models incorporated parameters such as ambient temperature, solar insolation, wind velocity, water temperature, and humidity. Among these, the one-degree regression models with three parameters outperformed the models with four or five parameters with a prediction error of 5.5 °C. Notably, the study found that the annual energy output estimates from the best model had an error margin of less than 0.2% compared to recorded data. Research indicates that solar PV panels over water bodies produce approximately 2.59% more annual energy output than ground-mounted systems. The newly developed regression models provide a predictive tool for estimating the operating temperature of solar PV installations above water bodies, using only three meteorological parameters: ambient temperature, solar insolation, and wind velocity, for accurate temperature prediction.