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

Author

Listed:
  • Gabriel Kasmi

    (MINES Paris, Université PSL Centre Observation Impacts Energie (O.I.E.), 06904 Sophia-Antipolis, France
    Direction de la Recherche et du Développement, RTE France, 92073 Paris La Défense, 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
    WEMC (World Energy & Meteorology Council), Norwich NR4 7TJ, UK)

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.

Suggested Citation

  • Gabriel Kasmi & Augustin Touron & Philippe Blanc & Yves-Marie Saint-Drenan & Maxime Fortin & Laurent Dubus, 2024. "Remote-Sensing-Based Estimation of Rooftop Photovoltaic Power Production Using Physical Conversion Models and Weather Data," Energies, MDPI, vol. 17(17), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4353-:d:1468155
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    References listed on IDEAS

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    1. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).
    2. Lu, Ning & Li, Liang & Qin, Jun, 2024. "PV Identifier: Extraction of small-scale distributed photovoltaics in complex environments from high spatial resolution remote sensing images," Applied Energy, Elsevier, vol. 365(C).
    3. Mayer, Kevin & Rausch, Benjamin & Arlt, Marie-Louise & Gust, Gunther & Wang, Zhecheng & Neumann, Dirk & Rajagopal, Ram, 2022. "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D," Applied Energy, Elsevier, vol. 310(C).
    4. Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
    5. Yang, Ruiqing & He, Guojin & Yin, Ranyu & Wang, Guizhou & Zhang, Zhaoming & Long, Tengfei & Peng, Yan, 2024. "Weakly-semi supervised extraction of rooftop photovoltaics from high-resolution images based on segment anything model and class activation map," Applied Energy, Elsevier, vol. 361(C).
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