Efficient estimation of convective cooling of photovoltaic arrays: A physics-informed machine learning approach

Convective cooling by wind is crucial for large-scale photovoltaic (PV) systems, as power generation inversely correlates with panel temperature. Therefore, accurately determining the convective heat transfer coefficient for PV arrays with various geometric configurations is essential to optimize array design. Traditional methods to quantify the effects of configuration utilize either Computational Fluid Dynamics (CFD) simulations or empirical methods. These approaches often face challenges due to high computational demands or limited accuracy, particularly with complex array configurations. Machine learning approaches, especially hybrid learning models, have emerged as effective tools to address challenges in heat transfer design optimization. This study introduces a method that combines Physics-Informed Machine Learning with a Deep Convolutional Neural Network (PIML-DCNN) to predict convective heat transfer rates with high accuracy and computational efficiency. Additionally, an innovative loss function, termed the ”Pocket Loss”, is developed to enhance the interpretability and robustness of the PIML-DCNN model. The proposed model achieves relative estimation errors of 2.5% and 2.7% on the validation and test datasets, respectively, when benchmarked against comprehensive CFD simulations. These results highlight the potential of the proposed model to efficiently guide the configuration design of PV arrays, thereby enhancing power generation in real-world operations. • A Physics-Informed Machine Learning model estimates heat dissipation in PV arrays. • Data from 160 CFD simulations is used to train and validate the proposed model. • A novel Pocket Loss function improves interpretability and model robustness. • The model achieves 2.7% errors on testing datasets. • The model outperforms empirical methods and is faster than CFD simulations.