Table 1.
Key contributions of the proposed study and literature limitations.
| Study (ref) | Cooling approach | Working fluid/nanofluid type | Operating conditions | Inputs/targets | ML/DL method and validation | Best metrics | Literature limitations addressed |
|---|---|---|---|---|---|---|---|
| 19 | Solar adsorption cooling | Al2O3, TiO2 - water | Outdoor experimental | Amb. Temp, Rad/thermal COP | Experimental/analytical | R2>0.90 (Exp. correlation) | Lack of data-driven predictive modeling for performance forecasting |
| 27 | PV/T cooling (review) | Various (SiC, Al2O3, CuO) | Review-based | N/A | Review study | N/A (review study) | No direct experimental modeling or Deep Learning (DL) application |
| 11 | PVT collector cooling | Water (conventional) | Indoor simulated | Inlet Temp, Flow/Tout | ML + genetic algorithm | R2=0.999, RMSE = 0.012 | Focuses on conventional fluids; lacks material and concentration diversity |
| 12 | Photo-thermal system | Water/glycol | Indoor/outdoor mix | Solar flux, Flow/Temp gain | Integrated ML (cross-validation) | R2=0.998, MSE = 0.004 | Nanofluid cooling and varying material concentrations not considered |
| 15 | PVT + geothermal | Water (conventional) | Outdoor experimental | Amb. Temp, wind/efficiency | ML predictive modeling | R2=0.985, MAE = 0.45 | Did not utilize hybrid deep learning architectures or nanofluids |
| 17 | hPVT system | Water/Al2O3 | Outdoor experimental | Radiation, wind/power yield | Gaussian process regression | R2=0.992, RMSE = 0.15 | Lack of hybrid DL (CNN/LSTM) for temporal feature extraction |
| 18 | Heat-pipe PV cooling | Al2O3 - water | Outdoor experimental | Rad, Amb. Temp/Tpanel | Hybrid ANN-PSO (train/test) | RMSE = 3.95 W | Limited to a single nanofluid; utilizes shallow hybrid ML models |
| 28 | PV/T cooling | ZnO - water | Outdoor experimental | Solar Rad, Flow/Tpanel | Experimental analysis | Error < 5% (uncertainty) | Lacks predictive algorithms and Machine Learning (ML) integration |
| 29 | PV/T cooling | SiO2 - water | Outdoor experimental | Solar Rad, Temp/efficiency | ML Regression (K-fold) | R2=0.989, MSE = 0.021 | Hybrid temporal deep learning (CNN+LSTM) not explored. |
| Proposed study | Active PV cooling | Water + Al2O3 & TiO2 | Real outdoor experimental | Rad, Tamb, Wind, V, I/efficiency | Hybrid CNN+LSTM, Bayesian Ridge, Random Forest | R2=0.981, RMSE = 0.28, MAE = 0.19 | Addresses: (i) Multi-material/concentration comparison; (ii) Hybrid DL (CNN+LSTM) architecture; (iii) High-fidelity outdoor benchmarking |