Performance Modeling of Rooftop PV Systems in Arid Climate, a Case Study for Qatar: Impact of Soiling Losses and Albedo Using PVsyst and SAM

Sachin Jain (), Mohamed Abdelrahim, Amir A. Abdallah, Dhanup S. Pillai and Sertac Bayhan
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Sachin Jain: Qatar Environment & Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 34110, Qatar
Mohamed Abdelrahim: Qatar Environment & Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 34110, Qatar
Amir A. Abdallah: Qatar Environment & Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 34110, Qatar
Dhanup S. Pillai: Qatar Environment & Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 34110, Qatar
Sertac Bayhan: Qatar Environment & Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 34110, Qatar

Energies, 2025, vol. 18, issue 22, 1-14

Abstract: This study presents a comparative performance modeling and optimization framework for a 5 kWp rooftop photovoltaic (PV) system in Qatar, using two widely adopted simulation tools, PVsyst and the System Advisor Model (SAM). The research addresses a key limitation in existing PV modeling practice: the restricted capability of standard software to represent site-specific soiling and dynamic albedo effects under arid climatic conditions. To overcome these limitations, the Humboldt State University (HSU) soiling model was calibrated using field measurements from a DustIQ sensor, and its parameters, rainfall cleaning threshold and particulate deposition velocity were optimized through a Differential Evolution algorithm. Additionally, the study utilized dynamic albedo inputs to better account for ground-reflectance effects in energy yield simulations. The optimized approach reduced the root mean square error (RMSE) of predicted soiling ratios from 7.30 to 1.93 and improved the agreement between simulated and measured monthly energy yields for 2024, achieving normalized RMSE values of 4.66% in SAM and 4.86% in PVsyst. The findings demonstrate that coupling data-driven soiling optimization with refined albedo representation modernizes the predictive capabilities of PVsyst and SAM, yielding more reliable performance forecasts. This methodological advancement supports better-informed design and operation of rooftop PV systems in desert environments where soiling and reflectivity effects are pronounced.

Keywords: photovoltaic performance modeling; soiling loss; arid climate; rooftop PV; HSU soiling model; Differential Evolution (DE); PVsyst; System Advisor Model (SAM); albedo; DustIQ sensor (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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