Modeling Energy and Exergy Efficiency of Photovoltaic Panels With Multivariate Regression Analysis Method: Applied Field Experiment Approach

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Akhan H.

International Journal of Photoenergy, cilt.1, sa.2026, ss.1-31, 2026 (SCI-Expanded)

Özet

In this study, the energy and exergy performance of photovoltaic (PV) panels was evaluated using a field-experiment-based applied approach and a multivariate regression model, considering environmental variables such as panel material type, tilt angle, surface temperature, solar radiation, wind speed, ambient temperature, relative humidity, and pressure. Additionally, interaction terms were included in the model to test the interactions among the variables. In literature, these parameters are mostly treated independently; this hinders a holistic analysis of PV systems under complex, multiple interactions. In this context, the originality of this study lies in its ability to numerically model the effects of multiple physical and environmental factors simultaneously and to statistically quantify the contribution of each factor to system performance. As part of the applied field experiment, measurements were conducted using monocrystalline and polycrystalline PV panels positioned at different tilt angles. Data were collected at 1-min sampling intervals, and the electrical efficiency generated simultaneously was calculated. After calculating energy and exergy efficiencies based on thermodynamic equations, the factors affecting these efficiencies were statistically tested using a multivariate regression method. It was determined that the model has high explanatory power (R2 > 0.8) and that the variables of solar radiation, panel type, and temperature have a significant effect on energy and exergy efficiency. The findings indicate that the design of PV systems must consider not only panel type, panel temperature, or a fixed tilt angle, but also actual environmental conditions. Furthermore, the regression model developed enables efficiency predictions tailored to different climate and installation scenarios and contributes to decision-support processes for selecting optimal tilt angles and materials. In this regard, the study represents a practical, sustainable, and original contribution that integrates a field-application-based approach with analytical modeling.