DOI: https://doi.org/10.61435/ijred.2026.62780
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Experimental and machine learning-based electro-thermal performance enhancement of PV/T renewable energy systems with advanced cooling and power optimization techniques for sustainable buildings

Electrical Engineering Department, Faculty of Engineering, Al-Baha University, Al Baha, Saudi Arabia

Received: 17 May 2026; Revised: 19 Sep 2026; Accepted: 10 Oct 2026; Available online: 18 Oct 2026; Published: 1 Nov 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2026 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
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Abstract

The electrical power output and conversion efficiency of photovoltaic (PV) systems decrease significantly due to increased thermal losses at elevated module temperatures. Unlike conventional PV/T enhancement approaches, this study integrates recycled porous media, hybrid nanofluid PCM cooling, and data-driven optimization within a unified electro-thermal framework. This work experimentally investigates and optimizes the electro-thermal performance of a PV/T renewable energy system under real climatic conditions of Madinah, Saudi Arabia. An extreme gradient boosting (XGBoost) machine learning model was employed to predict PV/T electrical and thermal performance and identify the dominant parameters affecting power generation and system efficiency. Four modified PV/T configurations were developed and compared with a conventional PV unit: Al₂O₃/water nanofluid cooling, recycled copper porous media, PCM containing recycled copper filings, and Cu/water nanofluid integrated with PCM. These configurations significantly enhance heat transfer coefficients and thermal dissipation, leading to improved electrical performance. The results revealed that the mean surface temperatures of PV/T@Al2O3, PV/T@Al2O3+CP, PV/T@Al2O3+CPPCM, PV/T@Cu+CPPCM were reduced by 6.08% (3.16 oC), 10.30% (5.15 oC), 15.31% (8.17 oC), 17.38% (11.98 oC) compared to standard PV, respectively. Consequently, electrical energy production increased by 7.57%, 18.85%, 27.85%, and 31.49%, respectively, compared with the standard PV unit (610 Wh). The PV/T@Cu+CPPCM configuration achieved the highest electrical and thermal efficiencies of 15.90% and 24.85%, respectively, with an electrical efficiency improvement of up to 22.39%. Techno-economic analysis indicates significant improvements in LCOE and payback period by 47.37, 55.56, 64.71, and 86.67%, respectively. The XGBoost model demonstrated high predictive accuracy and robustness, confirming its capability for real-time performance prediction and system level optimization. The results demonstrate a strong coupling between thermal regulation and electrical output enhancement, confirming the effectiveness of the proposed electro-thermal optimization strategy. This work presents a novel integrated electro-thermal optimization approach that combines hybrid cooling techniques with data-driven modeling to maximize PV/T system performance under real operating conditions for sustainable buildings in Saudi Arabia.

Keywords: Photovoltaic/thermal (PV/T) systems; Renewable energy systems; Electrical power generation; Machine learning; Sustainable buildings; Techno-economic analysis.

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Section: Original Research Article
Language : EN
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