Remote-Sensing-Based Estimation of Rooftop Photovoltaic Power Production Using Physical Conversion Models and Weather Data

Gabriel Kasmi, Augustin Touron, Philippe Blanc, Yves-Marie Saint-Drenan, Maxime Fortin and Laurent Dubus ()
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Gabriel Kasmi: MINES Paris, Université PSL Centre Observation Impacts Energie (O.I.E.), 06904 Sophia-Antipolis, France
Augustin Touron: Direction de la Recherche et du Développement, RTE France, 92073 Paris La Défense, France
Philippe Blanc: MINES Paris, Université PSL Centre Observation Impacts Energie (O.I.E.), 06904 Sophia-Antipolis, France
Yves-Marie Saint-Drenan: MINES Paris, Université PSL Centre Observation Impacts Energie (O.I.E.), 06904 Sophia-Antipolis, France
Maxime Fortin: Direction de la Recherche et du Développement, RTE France, 92073 Paris La Défense, France
Laurent Dubus: Direction de la Recherche et du Développement, RTE France, 92073 Paris La Défense, France

Energies, 2024, vol. 17, issue 17, 1-22

Abstract: The global photovoltaic (PV) installed capacity, vital for the electric sector’s decarbonation, reached 1552.3 GW p in 2023. In France, the capacity stood at 19.9 GW p in April 2024. The growth of the PV installed capacity over a year was nearly 32% worldwide and 15.7% in France. However, integrating PV electricity into grids is hindered by poor knowledge of rooftop PV systems, constituting 20% of France’s installed capacity, and the lack of measurements of the production stemming from these systems. This problem of lack of measurements of the rooftop PV power production is referred to as the lack of observability. Using ground-truth measurements of individual PV systems, available at an unprecedented temporal and spatial scale, we show that by estimating the PV power production of an individual rooftop system by combining solar irradiance and temperature data, the characteristics of the PV system inferred from remote sensing methods and an irradiation-to-electric power conversion model provides accurate estimations of the PV power production. We report an average estimation error (measured with the pRMSE) of 10% relative to the system size. Our study shows that we can improve rooftop PV observability, and thus its integration into the electric grid, using little information on these systems, a simple model of the PV system, and weather data. More broadly, this study shows that limited information is sufficient to derive a reasonably good estimation of the PV power production of small-scale systems.

Keywords: photovoltaic energy; PV power estimation; rooftop PV; remote sensing; conversion model (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: 2024
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