Machine Learning-Based Evaluation of Solar Photovoltaic Panel Exergy and Efficiency Under Real Climate Conditions

Abstract

The purpose of this study article is to provide a detailed examination of the performance of exergy electric panels, exergy efficiency panels and exergy solar panels under the climatic circumstances of the Utrecht region in the Netherlands. The study explores the performance of these solar panels in terms of both their energy efficiency and their exergy efficiency. Additionally, the study investigates critical factors such as solar radiation, module internal temperature, air temperature, maximum power, and solar energy efficiency. Environmental factors have a considerable impact on panel performance; temperature has a negative impact on efficiency, whereas an increase in solar radiation leads to an increase in energy and exergy output. These findings offer significant insights that can be used to increase the utilization of solar energy in locations that have a temperate oceanic climate, particularly in the context of the climatic conditions of the Utrecht region. The usefulness of the linear regression model in machine learning was validated by performance measures such as R2, RMSE, MAE, and MAPE. Furthermore, an R2 value of 0.94889 was found for the parameters that were utilized. Policy makers, researchers, and industry stakeholders who seek to successfully utilize solar energy in the face of changing climatic conditions may find this research to be an important reference.