Intelligent Hybrid PV–Thermal Systems: Machine Learning-Optimized Coolant Routing for Enhanced Energy Yield

Abstract: This study introduces an intelligent hybrid photovoltaic–thermal (PV–T) system incorporating machine learning-driven coolant routing optimization to maximize combined energy yield. Traditional PV–T configurations suffer from inefficiencies due to uneven temperature gradients and non-adaptive cooling strategies under varying irradiance conditions. By employing a supervised learning model that predicts optimal coolant distribution paths, the proposed system achieves enhanced heat transfer and stable electrical performance. Experimental validation under real-world conditions demonstrates a 17% improvement in overall efficiency and a 25% reduction in thermal stress, validating the system’s capability for adaptive energy optimization. Keywords: Intelligent hybrid PV–Thermal systems,Machine learning optimization,Coolant routing control,AI-driven renewable energy management,Energy yield enhancement, Digital twin–based thermal modeling