Predictive Modeling of Photovoltaic Thermal Systems: A Random Forest Regressor Approach for Enhanced Energy Output

This study investigates the Photovoltaic Thermal (PV/T) system’s efficiency in simultaneous electrical and thermal energy production, utilizing nanofluid coolants to cool temperature-sensitive PV cells. Filling a gap in existing models, a machine learning approach is examined to predict electrical and thermal power outputs specifically for a water-based PV/T system. Leveraging an extensive dataset of 15,540 water-based PV/T samples for training and testing, key thermophysical properties, time, and weather variables are considered as input features. The Random Forest Regressor model attains notable R2 values, reaching 0.9931 for electrical and 0.9852 for thermal predictions. Transitioning to nanofluid properties introduces a dynamic element, showcasing theoretical creativity and estimating electrical and thermal power outputs for Ag/water nanofluid-based PV/T systems. This study contributes to the advancement of predictive modeling in PV/T systems, demonstrating the model's accuracy and adaptability in the context of nanofluid-based energy production.