As the world moves towards renewable resources to meet the heaping power demand, the dependence on Photo Voltaic (PV) panel-based power generation has globally touched 1000TWh in 2021. These panels made up of different materials, such as Germanium (Ge), Silicon (Si), Indium Phosphide (InP), and Gallium Arsenide (GaAs), work in wider applications ranging from earth to space. But in real-time environmental conditions, these materials exhibit only 11 to 15 % efficiency, mainly due to thermal loss and exposure to variable irradiation and temperature conditions. Thus, in the current research, the PV cell/panel is modeled in an experimentally validated Multiphysics environment at temperatures from – 45 0C to + 51 0C using Ge, Si, InP, and GaAs as materials.The efficiency of the PV cell/panel is estimated in the current research based on the thermal losses within the material, the width of the bandgap, and the thickness of the cell. To analyze the thermal losses, joule heat generation of PV cell/panels made of all different materials is obtained. It is observed that at ambient temperature, although the GaAs and InP have an energy bandgap of 1.42 eV and 1.34 eV, respectively, the joule heating effect is minimum (3.95KW/m3 - InP, 5.05KW/m3- GaAs) when compared to Si (61KW/m3) and Ge (234KW/m3). But InP showed lower efficiency due to the thickness of the cell, which prevents the penetration of photons deeper into the inner layers. Further, in this research, the effect of real-time temperature conditions is obtained for all the PV panels by modeling them at −45 °C, 0 °C, 25 °C, and 51 °C. Parameters such as short circuit current (Isc), open circuit voltage (Voc), Fill Factor (FF), and maximum power (Pmax) are enumerated along with the efficiency. For a faster parametric analysis at all possible real-time temperature conditions in Indian climatic scenario, in this research, an ML based prediction model is developed using Kernel Ridge Regression (KRR), Polynomial Regression (PR), Linear Regression (LR), and Support Vector Regression (SVR). It is observed that, the KRR algorithm can give faster (in few seconds), effective, and error-free prediction (92.23% of accuracy). The prediction results are validated with the Multiphysics environment, data sheet, and experimental data.
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