A comprehensive review of machine learning applications in liquid-based cooling solutions of PV/T systems

Abstract

This paper presents a systematic review of machine learning (ML) applications in liquid-based photovoltaic-thermal (PV/T) systems, a topic that remains largely unaddressed in the existing review literature despite the growing importance of these hybrid systems in renewable energy. A total of 72 publications are analyzed and categorized across three methodological families: artificial neural networks (ANNs), ensemble methods, and other ML techniques. The review is complemented by a patent landscape analysis covering liquid-based PV/T technologies and a critical assessment of the experimental foundations underlying the reviewed ML models. The analysis reveals that ANNs dominate PV/T modeling at 63% of reviewed studies, with Multilayer Perceptron being the most frequently applied architecture. Ensemble methods, particularly Random Forest and XGBoost, achieve the highest prediction accuracies with R2 values up to 0.999. Across all ML categories, prediction accuracies exceed R2 = 0.95 in most applications, confirming the effectiveness of ML in capturing the complex thermal-electrical interactions characteristic of PV/T systems. Nanofluid-enhanced and phase change material configurations consistently demonstrate significant performance improvements over conventional water cooling. The review traces the evolution of ML methods in PV/T research from foundational ANN studies in 2012 through recent Transformer-based and reinforcement learning architectures in 2025. Critical research gaps are identified, including prevalent small dataset sizes in most studies, limited experimental validation of nanofluid ML models, and the absence of ML-related patent activity indicating a disconnect between academic research and commercial deployment. Future research directions are proposed covering standardized datasets, transfer learning, IoT integration for real-time control, and explainable AI for engineering interpretation.