Theoretical and Measured PV System Output Using High-Resolution Data: Analysis and Comparative Performance

The University of Jordan, School of Engineering, Department of Electrical Engineering, Amman, Jordan 11942

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Abstract

This paper presents a comparative performance analysis of a photovoltaic (PV) system using two distinct approaches; a mathematical model approach, based on standard test conditions and solar geometry using a cosine-squared function, and an experimental model approach, based on high resolution field measurements. The cosine-squared model was employed to simulate ideal clear-sky output based on actual sunrise and sunset times. AC power monitoring is conducted through five-minute interval basis across multiple seasons in 2023. The comparison is conducted on two large-scale PV subsystems (Car-Park-Mounted and Ground-Mounted subsystems) installed at the University of Jordan–Aqaba Branch. For this purpose, monthly energy yield, specific yield, and performance ratio are evaluated over a one-year period. Seasonal variations, efficiency trends, and potential losses due to real-world operating conditions were also identified. The results demonstrate that the Ground-Mounted system consistently outperformed the Car-Park-Mounted configuration, particularly under low-sun-angle or diffuse-light conditions, with efficiency differences, reaching up to 30%. Additionally, the analysis quantified the overestimation of energy in raw data, showing that daily energy can be inflated by nearly 9% if smoothing of raw measurements is not applied. These findings highlight the importance of combining theoretical modeling with high-resolution measurements to better interpret system performance, inform design decisions, and optimize operation under varying environmental conditions. The results not only validate the accuracy of the theoretical model but also highlight areas where environmental and operational factors cause significant deviations, providing actionable insights for PV system optimization in similar climatic contexts.