Flat vs. Curved: Machine Learning Classification of Flexible PV Panel Geometries

Ahmad Manasrah (), Yousef Jaradat, Mohammad Masoud, Mohammad Alia, Khaled Suwais and Piero Bevilacqua
Additional contact information
Ahmad Manasrah: Mechanical Engineering Department, Al-Zaytoonah University of Jordan, Amman P.O. Box 11733, Jordan
Yousef Jaradat: Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman P.O. Box 11733, Jordan
Mohammad Masoud: Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman P.O. Box 11733, Jordan
Mohammad Alia: Cybersecurity Department, Al-Zaytoonah University of Jordan, Amman P.O. Box 11733, Jordan
Khaled Suwais: Faculty of Computer Studies, Arab Open University, Riyadh 11681, Saudi Arabia
Piero Bevilacqua: Department of Mechanical, Energetic and Management Engineering, University of Calabria, 87036 Arcavacata, Italy

Energies, 2025, vol. 18, issue 13, 1-16

Abstract: As the global demand for clean and sustainable energy grows, photovoltaics (PVs) have become an important technology in this industry. Thin-film and flexible PV modules offer noticeable advantages for irregular surface mounts and mobile applications. This study investigates the use of four machine learning models to detect different flexible PV module geometries based on power output data. Three identical flexible PV modules were mounted in flat, concave, and convex configurations and connected to batteries via solar chargers. The experimental results showed that all geometries fully charged their batteries within 6–7 h on a sunny day with the flat, concave-, and convex-shaped modules achieving a peak power of 95 W. On a cloudy day, the concave and convex modules recorded peak outputs of 72 W and 65 W, respectively. Simulation results showed that the XGBoost model delivered the best classification performance, showing 93% precision with the flat-mounted module and 98% recall across all geometries. In comparison, the KAN model recorded the lowest precision (78%) with the curved geometries. A calibration analysis on the ML models showed that Random Forest and XGBoost were well calibrated for the flat-mounted module. However, they also showed overconfidence and underconfidence issues with the curved module geometries.

Keywords: flexible PV; geometry classification; XGBoost; Random Forest; Kolmogorov–Arnold Network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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