Experimental exergy analysis of SnO2 nanofluid photovoltaic thermal system using machine learning approach

The efficiency of photovoltaic thermal (PVT) systems is often hindered by high operating temperatures, which can be effectively addressed through advanced cooling methods. This study explored the use of a water-based tin dioxide (SnO<inf>2</inf>) nanofluid at a 0.1% concentration as an enhanced coolant to boost the system’s exergy efficiency. The research involved experimental testing under three distinct flow rates—0.5, 1.0 and 1.5 LPM—to evaluate the nanofluid’s performance. The results confirmed that the nanofluid offered a significant advantage over conventional pure water cooling. Specifically, at the highest flow rate of 1.5 LPM, the maximum exergy efficiency improved remarkably from 11.1 to 18.9%. In addition to the experimental work, the study also developed and tested several machine learning (ML) models to predict the system’s performance. Two primary models, K-Nearest Neighbor (KNN) and Support Vector Regression (SVR), were utilized. The researchers also investigated the impact of integrating Wavelet Transform (WT), a signal-processing technique, with these ML models. The results demonstrated that the SVR model combined with Wavelet Transform (SVR-WT) provided the most accurate predictions on the test dataset. This model achieved an impressive coefficient of determination (R2) of 0.885, indicating a strong correlation between the predicted and actual values. Its predictive capability was further highlighted by a low root mean square error (RMSE) of 2.196 and a mean absolute error (MAE) of 3.086. Overall, the findings conclusively establish that SnO2 nanofluid is an excellent coolant for enhancing PVT system performance, and that the SVR-WT model offers a reliable predictive framework for optimizing these systems.