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Ener3DMap-SolarWeb roofs: A geospatial web-based platform to compute photovoltaic potential

Author

Listed:
  • Sánchez-Aparicio, M.
  • Martín-Jiménez, J.
  • Del Pozo, S.
  • González-González, E.
  • Lagüela, S.

Abstract

The effective exploitation and management of renewable energies requires knowledge not only of the energy intensity at the exploitation site but also of the influence of the geometry of the site and its surroundings. For this reason, the efficient processing and interpretation of combined geospatial and energy data is a key issue. This paper presents the development of a web-based tool for the automatic computation of photovoltaic potential on rooftops and on parcels without buildings. The tool called Ener3DMap-SolarWeb Roofs is based on Leaflet and supports WMS, GeoJSON, GeoCSV and KML formats, among others. With these data formats, base maps, geometric data from the rooftops automatically computed from LiDAR and imagery data with self-developed processing algorithms, cadastral data and a solar radiation model are integrated in the tool. These different types of data, the high level of automation and the different scales for which energy data is calculated (hourly, monthly and annually) are the main contributions of the presented tool compared to other existing solutions. The capacities of the tool are tested through its application to analyze the solar potential of rooftops with different shapes and for different solar panel configurations. The accuracy of the results is ensured through the integration of a validated methodology for the computation of geometry and a validated solar radiation model, PVGIS.

Suggested Citation

  • Sánchez-Aparicio, M. & Martín-Jiménez, J. & Del Pozo, S. & González-González, E. & Lagüela, S., 2021. "Ener3DMap-SolarWeb roofs: A geospatial web-based platform to compute photovoltaic potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:rensus:v:135:y:2021:i:c:s1364032120304937
    DOI: 10.1016/j.rser.2020.110203
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    Cited by:

    1. Chen, Zhe & Yang, Bisheng & Zhu, Rui & Dong, Zhen, 2024. "City-scale solar PV potential estimation on 3D buildings using multi-source RS data: A case study in Wuhan, China," Applied Energy, Elsevier, vol. 359(C).
    2. Agnieszka Bieda & Agnieszka Cienciała, 2021. "Towards a Renewable Energy Source Cadastre—A Review of Examples from around the World," Energies, MDPI, vol. 14(23), pages 1-34, December.
    3. Aslani, Mohammad & Seipel, Stefan, 2022. "Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment," Applied Energy, Elsevier, vol. 306(PA).
    4. Agbonaye, Osaru & Keatley, Patrick & Huang, Ye & Ademulegun, Oluwasola O. & Hewitt, Neil, 2021. "Mapping demand flexibility: A spatio-temporal assessment of flexibility needs, opportunities and response potential," Applied Energy, Elsevier, vol. 295(C).
    5. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).
    6. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).

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