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Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)

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  • Jacques, David A.
  • Gooding, James
  • Giesekam, Jannik J.
  • Tomlin, Alison S.
  • Crook, Rolf

Abstract

An assessment of roof-mounted PV capacity over a local region can be accurately calculated by established roof segmentation algorithms using high-resolution light detection and ranging (LiDAR) datasets. However, over larger city regions often only low-resolution LiDAR data is available where such algorithms prove unreliable for small rooftops. A methodology optimised for low-resolution LiDAR datasets is presented, where small and large buildings are considered separately. The roof segmentation algorithm for small buildings, which are typically residential properties, assigns a roof profile to each building from a catalogue of common profiles after identifying LiDAR points within the building footprint. Large buildings, such as warehouses, offer a more diverse range of roof profiles but geometric features are generally large, so a direct approach is taken to segmentation where each LiDAR point within the building footprint contributes a separate roof segment. The methodology is demonstrated by application to the city region of Leeds, UK. Validation by comparison to aerial photography indicates that the assignment of an appropriate roof profile to a small building is correct in 81% of cases.

Suggested Citation

  • Jacques, David A. & Gooding, James & Giesekam, Jannik J. & Tomlin, Alison S. & Crook, Rolf, 2014. "Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)," Applied Energy, Elsevier, vol. 124(C), pages 28-34.
    • Handle: RePEc:eee:appene:v:124:y:2014:i:c:p:28-34
    DOI: 10.1016/j.apenergy.2014.02.076
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    References listed on IDEAS

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    8. Jiang, Mingkun & Qi, Lingfei & Yu, Ziyi & Wu, Dadi & Si, Pengfei & Li, Peiran & Wei, Wendong & Yu, Xinhai & Yan, Jinyue, 2021. "National level assessment of using existing airport infrastructures for photovoltaic deployment," Applied Energy, Elsevier, vol. 298(C).
    9. Buffat, René & Grassi, Stefano & Raubal, Martin, 2018. "A scalable method for estimating rooftop solar irradiation potential over large regions," Applied Energy, Elsevier, vol. 216(C), pages 389-401.
    10. Gooding, James & Crook, Rolf & Tomlin, Alison S., 2015. "Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method," Applied Energy, Elsevier, vol. 148(C), pages 93-104.
    11. Zhixin Li & Chen Zhang & Zejun Yu & Hong Zhang & Haihua Jiang, 2023. "Deep Learning Method for Evaluating Photovoltaic Potential of Rural Land Use Types," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    12. 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.
    13. Hannes Koch & Stefan Lechner & Sebastian Erdmann & Martin Hofmann, 2022. "Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany," Energies, MDPI, vol. 15(19), pages 1-17, September.
    14. Bórawski, Piotr & Holden, Lisa & Bełdycka-Bórawska, Aneta, 2023. "Perspectives of photovoltaic energy market development in the european union," Energy, Elsevier, vol. 270(C).
    15. 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.
    16. Hou Jiang & Ning Lu & Xuecheng Wang, 2023. "Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    17. Primož Mavsar & Klemen Sredenšek & Bojan Štumberger & Miralem Hadžiselimović & Sebastijan Seme, 2019. "Simplified Method for Analyzing the Availability of Rooftop Photovoltaic Potential," Energies, MDPI, vol. 12(22), pages 1-17, November.
    18. 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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