PV Systems Efficiency Evaluation Using Machine Learning Techniques

Abstract

Many factors, such as solar irradiance, temperature, angle of incidence, shading, panel degradation, temperature coefficient, and other parameters, can substantially affect the efficiency of photovoltaic (PV) systems. This work presents a concise overview of cooling strategies, temperature coefficient analysis, geographical influence, and advanced materials to explain the effects of solar irradiance and ambient temperature on the efficiency of PV systems. Furthermore, the study examines the two factors of solar irradiance and ambient temperature using two distinct approaches: deterministic techniques and machine learning methods. The results demonstrate that solar irradiance and temperature significantly impact the efficiency of PV models. These results lead to enhancing the performance and reliability of solar energy systems.