Enhancing energy conversion in photovoltaic-thermal systems through nanoparticle optimization

This research investigates the integration of nanoparticle additives into Photovoltaic-Thermal (PVT) systems to improve energy conversion efficiency and overall performance. Using advanced computational fluid dynamics (CFD) simulations in ANSYS, we examined the thermal and electrical efficiencies of various PVT configurations using Aluminum Oxide (Al₂O₃) and Titanium Dioxide (TiO₂) nanoparticles. Our simulation and experimental validation findings indicate substantial improvements, with optimized systems achieving electrical and thermal efficiencies of 19.7% and 70%, respectively. These results demonstrate the potential of hybrid nanofluid cooling to enhance the thermoelectric performance of PVT systems under the operating conditions investigated. Additionally, a hybrid machine learning framework combining XGBoost and a Multi-Layer Perceptron (MLP) effectively minimized prediction errors, yielding a Mean Absolute Error (MAE) of 0.5% and a Root Mean Square Error (RMSE) of 0.3%. This underscores the robust predictive capabilities concerning thermal performance parameters. The results contribute significant insights into the synergistic role of nanomaterials in renewable energy systems, paving the path for advancements in smart energy solutions.