Solar Irradiance Database Comparison for PV System Design: A Case Study

Jamal AlFaraj (), Emanuel Popovici and Paul Leahy
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Jamal AlFaraj: School of Engineering & Architecture, University College Cork, College Road, T12 HW58 Cork, Ireland
Emanuel Popovici: School of Engineering & Architecture, University College Cork, College Road, T12 HW58 Cork, Ireland
Paul Leahy: School of Engineering & Architecture, University College Cork, College Road, T12 HW58 Cork, Ireland

Sustainability, 2024, vol. 16, issue 15, 1-26

Abstract: Effective design of solar photovoltaic (PV) systems requires accurate meteorological data for solar irradiance, ambient temperature, and wind speed. In this study, we aim to assess the reliability of satellite-based solar resource databases such as NASA, Solcast, and PVGIS by comparing them with ground-based measurements of global horizontal irradiance (GHI) from six locations in the Republic of Ireland. We compared satellite- and ground-based GHI data recorded between 2011 and 2012 and used Python-based packages to simulate solar power output for the six locations using both data types. The simulated outputs were then compared against metered power output from PV arrays at the sites. Ground-based GHI measurements demonstrate superior accuracy due to their acquisition at specific locations, offering increased spatial representativity. On the other hand, satellite GHI measurements, although reasonably accurate for many applications, cover broader regions with lower spatial resolution, leading to averaging effects that may not fully capture localized variations. This difference is reflected in the mean absolute percentage error (MAPE) values, with ground-simulated data showing low MAPE values, indicating strong alignment with reference observations, while satellite-simulated data exhibit a slightly higher MAPE, suggesting less precise estimates despite a strong correlation with ground-based measurements. This study demonstrates the relative reliability of satellite- and ground-based GHI data for accurate solar PV system design, emphasizing the practical implications for energy planners and engineers, and providing a strong enhancement for researchers working on forecasting solar energy yields using satellite databases. The Python-based PVLib package was utilized for the simulation, offering a robust framework for modeling and analyzing solar power systems, and its effectiveness in this context is discussed in detail.

Keywords: solar database; energy sources; satellite data validation; meteorological data; Python-PVLib power simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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