Abstract
This study investigates the enhancement of Photovoltaic-Thermal (PVT) collector performance through the combined use of water-based manganese dioxide (MnO(2)) nanofluids and machine learning (ML) models. Conventional PVT systems often suffer from elevated operating temperatures that degrade photovoltaic efficiency. To address this challenge, the research employs MnO(2) nanoparticles-known for their stability, cost-effectiveness, and high thermal conductivity-dispersed in water to improve thermal regulation within the PVT system. Experimental evaluations were conducted at three flow rates (0.5, 1.0, and 1.5 LPM) to assess thermal and electrical performance. The MnO(2) nanofluid-based PVT collector demonstrated superior power output (ranging from 80.42 W to 202.91 W) compared to water-cooled PVT (72.48 W to 176.17 W) and standalone PV systems (64.23 W to 152.36 W). A peak electrical efficiency of 14.58% was observed at 0.5 LPM, while glazing surface temperatures during midday ranged between 52.03 °C and 54.60 °C, indicating effective thermal management. To predict system behavior and performance, machine learning models-including Random Forest (RF), Radial Basis Function (RBF), and Multilayer Perceptron (MLP)-were applied. Among these, the RBF model achieved the highest predictive accuracy, with R(2) values of 0.96 for power output and 0.97 for electrical efficiency on the testing dataset. Overall, this integrated experimental-ML approach not only confirms the thermal and electrical advantages of MnO(2) nanofluids but also demonstrates the potential for intelligent optimization and control in high-performance solar energy systems.