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Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data

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  • Lukač, Niko
  • Seme, Sebastijan
  • Žlaus, Danijel
  • Štumberger, Gorazd
  • Žalik, Borut

Abstract

One of the major challenges today is assessing the suitability of PV (photovoltaic) systems' installations on buildings' roofs regarding the received solar irradiance. The availability of aerial laser-scanning, namely LiDAR (Light Detection And Ranging), means that assessment can be performed automatically over large-scale urban areas in high accuracy by considering surfaces' topographies, long-term direct and diffuse irradiance measurements, and influences of shadowing. The solar potential metric was introduced for this purpose, however it fails to provide any insights into the production of electrical energy by a specific PV system. Hence, the PV potential metric can be used that integrates received instantaneous irradiance which is then multiplied by the PV system's efficiency characteristics. Many existing PV potential metrics over LiDAR data consider the PV modules' efficiencies to be constant, when in reality they are nonlinear. This paper presents a novel PV potential estimation over LiDAR data, where the PV modules' and solar inverter's nonlinear efficiency characteristics are approximated by modelled functions. The estimated electrical energy production from buildings' roofs within an urban area was extensively analysed by comparing the constant and nonlinear efficiency characteristics of different PV module types and solar inverters. The obtained results were confirmed through measurements performed on an existing PV system.

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  • Lukač, Niko & Seme, Sebastijan & Žlaus, Danijel & Štumberger, Gorazd & Žalik, Borut, 2014. "Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data," Energy, Elsevier, vol. 66(C), pages 598-609.
    • Handle: RePEc:eee:energy:v:66:y:2014:i:c:p:598-609
    DOI: 10.1016/j.energy.2013.12.066
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    1. Elham Fakhraian & Marc Alier & Francesc Valls Dalmau & Alireza Nameni & Maria José Casañ Guerrero, 2021. "The Urban Rooftop Photovoltaic Potential Determination," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    2. Kovač, Marko & Stegnar, Gašper & Al-Mansour, Fouad & Merše, Stane & Pečjak, Andrej, 2019. "Assessing solar potential and battery instalment for self-sufficient buildings with simplified model," Energy, Elsevier, vol. 173(C), pages 1182-1195.
    3. Özdemir, Samed & Yavuzdoğan, Ahmet & Bilgilioğlu, Burhan Baha & Akbulut, Zeynep, 2023. "SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data," Renewable Energy, Elsevier, vol. 216(C).
    4. Sredenšek, Klemen & Štumberger, Bojan & Hadžiselimović, Miralem & Mavsar, Primož & Seme, Sebastijan, 2022. "Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method," Energy, Elsevier, vol. 242(C).
    5. Abdulsalam S. Alghamdi & AbuBakr S. Bahaj & Yue Wu, 2017. "Assessment of Large Scale Photovoltaic Power Generation from Carport Canopies," Energies, MDPI, vol. 10(5), pages 1-22, May.
    6. Srećković, Nevena & Lukač, Niko & Žalik, Borut & Štumberger, Gorazd, 2016. "Determining roof surfaces suitable for the installation of PV (photovoltaic) systems, based on LiDAR (Light Detection And Ranging) data, pyranometer measurements, and distribution network configuratio," Energy, Elsevier, vol. 96(C), pages 404-414.
    7. Meskiana Boulahia & Kahina Amal Djiar & Miguel Amado, 2021. "Combined Engineering—Statistical Method for Assessing Solar Photovoltaic Potential on Residential Rooftops: Case of Laghouat in Central Southern Algeria," Energies, MDPI, vol. 14(6), pages 1-16, March.
    8. Mohajeri, Nahid & Assouline, Dan & Guiboud, Berenice & Bill, Andreas & Gudmundsson, Agust & Scartezzini, Jean-Louis, 2018. "A city-scale roof shape classification using machine learning for solar energy applications," Renewable Energy, Elsevier, vol. 121(C), pages 81-93.
    9. Phuong Minh Khuong & Russell McKenna & Wolf Fichtner, 2020. "A Cost-Effective and Transferable Methodology for Rooftop PV Potential Assessment in Developing Countries," Energies, MDPI, vol. 13(10), pages 1-46, May.
    10. Shakouri, Mahmoud & Lee, Hyun Woo & Kim, Yong-Woo, 2017. "A probabilistic portfolio-based model for financial valuation of community solar," Applied Energy, Elsevier, vol. 191(C), pages 709-726.
    11. 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.
    12. Lukač, Niko & Špelič, Denis & Štumberger, Gorazd & Žalik, Borut, 2020. "Optimisation for large-scale photovoltaic arrays’ placement based on Light Detection And Ranging data," Applied Energy, Elsevier, vol. 263(C).
    13. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    14. Wong, Man Sing & Zhu, Rui & Liu, Zhizhao & Lu, Lin & Peng, Jinqing & Tang, Zhaoqin & Lo, Chung Ho & Chan, Wai Ki, 2016. "Estimation of Hong Kong’s solar energy potential using GIS and remote sensing technologies," Renewable Energy, Elsevier, vol. 99(C), pages 325-335.
    15. Luis Ramirez Camargo & Judith Franco & Nilsa María Sarmiento Babieri & Silvina Belmonte & Karina Escalante & Raphaela Pagany & Wolfgang Dorner, 2016. "Technical, Economical and Social Assessment of Photovoltaics in the Frame of the Net-Metering Law for the Province of Salta, Argentina," Energies, MDPI, vol. 9(3), pages 1-21, February.
    16. 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.
    17. Fretzen, Ulrich & Ansarin, Mohammad & Brandt, Tobias, 2021. "Temporal city-scale matching of solar photovoltaic generation and electric vehicle charging," Applied Energy, Elsevier, vol. 282(PA).
    18. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
    19. Julieta, Schallenberg-Rodriguez & José-Julio, Rodrigo-Bello & Pablo, Yanez-Rosales, 2022. "A methodology to estimate the photovoltaic potential on parking spaces and water deposits. The case of the Canary Islands," Renewable Energy, Elsevier, vol. 189(C), pages 1046-1062.
    20. Lukač, Niko & Seme, Sebastijan & Dežan, Katarina & Žalik, Borut & Štumberger, Gorazd, 2016. "Economic and environmental assessment of rooftops regarding suitability for photovoltaic systems installation based on remote sensing data," Energy, Elsevier, vol. 107(C), pages 854-865.
    21. 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).
    22. 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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