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3D Solar Potential in the Urban Environment: A Case Study in Lisbon

Author

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  • Miguel Centeno Brito

    (Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749–016 Lisboa, Portugal)

  • Paula Redweik

    (Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749–016 Lisboa, Portugal)

  • Cristina Catita

    (Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749–016 Lisboa, Portugal)

  • Sara Freitas

    (Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749–016 Lisboa, Portugal)

  • Miguel Santos

    (Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749–016 Lisboa, Portugal)

Abstract

The assessment of solar potential in the urban environment is an important instrument for policy decision regarding renewable energy deployment in the city. This paper presents an experimentally validated 3D solar potential model for rooftops and facades from LIDAR data considering anisotropic diffuse irradiation. The data visualization is rendered in the ArcGIS platform using CityEngine to automatically generate 3D models from 2D geometries. The model is validated against summer and winter measurements of photovoltaic performance on a facade. A case study for two densely packed urban areas in Lisbon, Portugal, are presented. Facades are shown to increase the solar potential by 10 to 15%.

Suggested Citation

  • Miguel Centeno Brito & Paula Redweik & Cristina Catita & Sara Freitas & Miguel Santos, 2019. "3D Solar Potential in the Urban Environment: A Case Study in Lisbon," Energies, MDPI, vol. 12(18), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3457-:d:265184
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    References listed on IDEAS

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    1. Freitas, S. & Catita, C. & Redweik, P. & Brito, M.C., 2015. "Modelling solar potential in the urban environment: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 915-931.
    2. Freitas, S. & Brito, M.C., 2019. "Non-cumulative only solar photovoltaics for electricity load-matching," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 271-283.
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    3. Sebastian Krapf & Nils Kemmerzell & Syed Khawaja Haseeb Uddin & Manuel Hack Vázquez & Fabian Netzler & Markus Lienkamp, 2021. "Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning," Energies, MDPI, vol. 14(13), pages 1-22, June.
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    5. Marcela Bindzarova Gergelova & Slavomir Labant & Stefan Kuzevic & Zofia Kuzevicova & Henrieta Pavolova, 2020. "Identification of Roof Surfaces from LiDAR Cloud Points by GIS Tools: A Case Study of Lučenec, Slovakia," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
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    7. Drozd, Paweł & Kapica, Jacek & Jurasz, Jakub & Dąbek, Paweł, 2025. "Evaluating cities' solar potential using geographic information systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
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    9. Hélio Henrique Cunha Pinheiro & Neilton Fidélis da Silva & David Alves Castelo Branco & Márcio Giannini Pereira, 2020. "Photovoltaic Solar Systems in Multi-Headquarter Institutions: A Technical Implementation in Northeastern Brazil," Energies, MDPI, vol. 13(10), pages 1-28, May.
    10. Mrówczyńska, M. & Skiba, M. & Sztubecka, M. & Bazan-Krzywoszańska, A. & Kazak, J.K. & Gajownik, P., 2021. "Scenarios as a tool supporting decisions in urban energy policy: The analysis using fuzzy logic, multi-criteria analysis and GIS tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    11. Cheng, Liang & Zhang, Fangli & Li, Shuyi & Mao, Junya & Xu, Hao & Ju, Weimin & Liu, Xiaoqiang & Wu, Jie & Min, Kaifu & Zhang, Xuedong & Li, Manchun, 2020. "Solar energy potential of urban buildings in 10 cities of China," Energy, Elsevier, vol. 196(C).

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