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Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

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  • Fabio Giussani

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy
    Laboratorio di Simulazione Urbana Fausto Curti, Department of Architecture and Urban Studies (DASTU), Politecnico of Milan, Via Bonardi, 3, 20133 Milan, Italy)

  • Eric Wilczynski

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

  • Claudio Zandonella Callegher

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

  • Giovanni Dalle Nogare

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

  • Cristian Pozza

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

  • Antonio Novelli

    (RHEA Group, Via di Grotte Portella 28, Edificio Clorofilla, Scala C, Piano 3, 00044 Frascati, Italy)

  • Simon Pezzutto

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

Abstract

Locating and quantifying photovoltaic (PV) installations is a time-consuming and labor-intensive process, but it is necessary for monitoring their distribution. In the absence of existing data, the use of aerial imagery and automated detection algorithms can improve the efficiency and accuracy of the data collection process. This study presents a machine learning approach for the analysis of PV installations in urban areas based on less complex and resource-intensive models to target the challenge of data scarcity. The first objective of this work is to develop a model that can automatically detect PV installations from aerial imagery and test it based on the case study of Crevillent, Spain. Subsequently, the work estimates the PV capacity in Crevillent, and it compares the distribution of PV installations between residential and industrial areas. The analysis utilizes machine learning techniques and existing bottom-up data to assess land use and building typology for PV installations, identifying deployment patterns across the town. The proposed approach achieves an accuracy of 67% in detecting existing PV installations. These findings demonstrate that simple machine learning models still provide a reliable and cost-effective way to obtain data for decision-making in the fields of energy and urban planning, particularly in areas with limited access to existing data. Combining this technology with bottom-up data can lead to more comprehensive insights and better outcomes for urban areas seeking to optimize and decarbonize their energy supply while minimizing economic resources.

Suggested Citation

  • Fabio Giussani & Eric Wilczynski & Claudio Zandonella Callegher & Giovanni Dalle Nogare & Cristian Pozza & Antonio Novelli & Simon Pezzutto, 2024. "Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology," Sustainability, MDPI, vol. 16(5), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2020-:d:1348782
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    References listed on IDEAS

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    1. Ren, Simiao & Hu, Wayne & Bradbury, Kyle & Harrison-Atlas, Dylan & Malaguzzi Valeri, Laura & Murray, Brian & Malof, Jordan M., 2022. "Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis," Applied Energy, Elsevier, vol. 326(C).
    2. 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).
    3. 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.
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    1. Boris I. Evstatiev & Dimitar T. Trifonov & Katerina G. Gabrovska-Evstatieva & Nikolay P. Valov & Nicola P. Mihailov, 2024. "PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning," Energies, MDPI, vol. 17(20), pages 1-20, October.

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