  Convective heat transfer is crucial for photovoltaic (PV) systems, as the
power generation of PV is sensitive to temperature. The configuration of PV
arrays have a significant impact on convective heat transfer by influencing
turbulent characteristics. Conventional methods of quantifying the
configuration effects are either through Computational Fluid Dynamics (CFD)
simulations or empirical methods, which face the challenge of either high
computational demand or low accuracy, especially when complex array
configurations are considered. This work introduces a novel methodology to
quantify the impact of geometric configurations of PV arrays on their
convective heat transfer rate in wind field. The methodology combines Physics
Informed Machine Learning (PIML) and Deep Convolution Neural Network (DCNN) to
construct a robust PIML-DCNN model to predict convective heat transfer rates.
In addition, an innovative loss function, termed Pocket Loss is proposed to
enhance the interpretability of the PIML-DCNN model. The model exhibits
promising performance, with a relative error of 1.9\% and overall $R^2$ of 0.99
over all CFD cases in estimating the coefficient of convective heat transfer,
when compared with full CFD simulations. Therefore, the proposed model has the
potential to efficiently guide the configuration design of PV arrays for power
generation enhancement in real-world operations.
