Active cooling can mitigate temperature-induced performance losses in photovoltaic (PV) modules, and nanofluids are a promising coolant option. This study develops data-driven models to predict the cooling efficiency of an actively cooled PV panel using seven working fluids: water and Al₂O₃/TiO₂ nanofluids at 0.01%, 0.1%, and 1 vol%. For each fluid, outdoor measurements were collected over six hours at 30-min intervals (13 observations), including inlet/outlet temperatures, electrical variables of cooled and reference panels, and ambient conditions. Shallow regression models (Bayesian Ridge, SVR-RBF, Random Forest) were evaluated using leave-one-out cross-validation, and a hybrid deep learning (CNN+LSTM) model was also tested using k-fold cross-validation. Bayesian Ridge achieved the most consistent performance across materials (RMSE ≈ 0.35–0.66; R² ≈ 0.88–0.98). The hybrid CNN+LSTM reached RMSE as low as 0.28 with R² up to 0.98. SHAP-based interpretability analysis indicates that ambient temperature, irradiance, and the cooled-panel electrical variables are among the most influential predictors. These results show that lightweight ML models can reliably estimate PV cooling performance and reduce repetitive experimentation.
Ozdemir et al. (Fri,) studied this question.