Advancing Efficiency in PVT Solar Technology by Leveraging Artificial Intelligence in Intelligent Thermal Management

Abstract

Photovoltaic-Thermal (PVT) systems have a strong potential to improve solar technology in energy generation and conversion. The performance of PVT systems is, however, critically limited by the effect of elevated operating temperatures on photovoltaic efficiency under dynamic conditions. Traditional thermal management strategies limitedly address the non-linear, stochastic, and multi-objective challenges that are inherent to PVT system operation. This paper critically reviews the current application of Artificial Intelligence (AI) as a transformative technology for intelligent thermal management in PVT systems to improve PVT systems’ efficiency.We cover about 130 papers from the last decade, analysing the application of AI paradigms such as Artificial Neural Networks (ANNs), Support Vector Machines (SVM), Deep Reinforcement Learning (DRL) and Physics-Informed Neural Networks (PINNs) to solar PVT systems. The contribution of this work is its focus on thermal management that integrates modern concepts of edge AI, digital twins, and trustworthy AI. It also presents a rigorous comparative analysis of AI against traditional control methods. We also perform analysis through qualitative comparison tables of AI techniques and a visual taxonomy of AI applications. The key research gaps are identified in the study, including the scarcity of standardised validation datasets, the challenge of sim-to-real transfer and the need for a strong and computationally efficient edge deployment. The review then focuses on a strategic research roadmap which advocates for a focus on hybrid physics-AI models, verifiable digital twins, and explainable AI (XAI) to build strong, efficient, and autonomous PVT infrastructures.