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

Year: 2025

Type: article

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

Source: International Journal of Academic and Industrial Research Innovations(IJAIRI)

DOI: 10.62311/nesx/rp-6-sep-25

Author: Murali Krishna Pasupuleti

Language: English

Topic: Solar Thermal and Photovoltaic Systems

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: bronze

OpenAlex ID: W4415065220

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