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<p>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.</p>

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Comparative analysis of shallow and hybrid deep learning models for predicting the cooling efficiency of nanofluid-cooled photovoltaic panel across multiple materials

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月20日

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  • Yunus Özdemir,
  • Neşe Budak Ziyadanoğulları,
  • Enes Bakış,
  • Emrullah Acar

摘要

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.