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Solar Energy Assessment: From Rooftop Extraction to Identifying Utilizable Areas

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Geographical Information Systems Theory, Applications and Management (GISTAM 2021, GISTAM 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1908))

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Abstract

Rooftop photovoltaics have been acknowledged as a critical component in cities’ efforts to reduce their reliance on fossil fuels and move towards energy sustainability. Identifying rooftop areas suitable for installing rooftop photovoltaics-referred to as utilizable areas-is essential for effective energy planning and developing policies related to renewable energies. Utilizable areas are greatly affected by the size, shape, superstructures of rooftops, and shadow effects. This study estimates utilizable areas and solar energy potential of rooftops by considering the mentioned factors. First, rooftops are extracted from LiDAR data by training PointNet++, a neural network architecture for processing 3D point clouds. The second step involves extracting planar segments of rooftops using a combination of clustering and region growing. Finally, utilizable areas of planar segments are identified by removing areas that do not have a suitable size and do not receive sufficient solar irradiation. Additionally, in this step, areas reserved for accessibility to photovoltaics are removed. According to the experimental results, the methods have a high success rate in rooftop extraction, plane segmentation, and, consequently, estimating utilizable areas for photovoltaics.

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Authors and Affiliations

  1. Department of Computer and Geo-spatial Sciences, University of Gävle, Gävle, Sweden

    Mohammad Aslani & Stefan Seipel

  2. Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, Uppsala, Sweden

    Stefan Seipel

Authors
  1. Mohammad Aslani
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  2. Stefan Seipel
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Correspondence to Mohammad Aslani .

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Editors and Affiliations

  1. Polytechnic Institute of Setúbal/IPS, Setúbal, Portugal

    Cédric Grueau

  2. Knowledge Systems Institute, Skokie, IL, USA

    Robert Laurini

  3. ATHENA Research & Innovation Information Technologies, Marousi, Greece

    Lemonia Ragia

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Aslani, M., Seipel, S. (2023). Solar Energy Assessment: From Rooftop Extraction to Identifying Utilizable Areas. In: Grueau, C., Laurini, R., Ragia, L. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM GISTAM 2021 2022. Communications in Computer and Information Science, vol 1908. Springer, Cham. https://doi.org/10.1007/978-3-031-44112-7_7

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  • DOI: https://doi.org/10.1007/978-3-031-44112-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44111-0

  • Online ISBN: 978-3-031-44112-7

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