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Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method

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  • Gooding, James
  • Crook, Rolf
  • Tomlin, Alison S.

Abstract

This article describes a method to model roof geometries from widely available low-resolution (2m horizontal) Light Detection and Ranging (LiDAR) datasets for application on a city-wide scale. The model provides roof area, orientation, and slope, appropriate for predictions of solar technology performance, being of value to national and regional policy makers in addition to investors and individuals appraising the viability of specific sites. Where present, similar buildings are grouped together based on proximity and building footprint dimensions. LiDAR data from all the buildings in a group is combined to construct a shared high-resolution LiDAR dataset. The best-fit roof shape is then selected from a catalogue of common roof shapes and assigned to all buildings in that group. Method validation was completed by comparing the model output to a ground-based survey of 169 buildings and aerial photographs of 536 buildings, all located in Leeds, UK. The method correctly identifies roof shape in 87% of cases and the modelled roof slope has a mean absolute error of 3.76°. These performance figures are only possible when segmentation, similar building grouping and ridge repositioning algorithms are used.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:148:y:2015:i:c:p:93-104
    DOI: 10.1016/j.apenergy.2015.03.013
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    Cited by:

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    4. Martínez-Rubio, A. & Sanz-Adan, F. & Santamaría-Peña, J. & Martínez, Araceli, 2016. "Evaluating solar irradiance over facades in high building cities, based on LiDAR technology," Applied Energy, Elsevier, vol. 183(C), pages 133-147.
    5. 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.
    6. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. 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.
    8. Zhong, Teng & Zhang, Zhixin & Chen, Min & Zhang, Kai & Zhou, Zixuan & Zhu, Rui & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2021. "A city-scale estimation of rooftop solar photovoltaic potential based on deep learning," Applied Energy, Elsevier, vol. 298(C).
    9. Menglin Dai & Wil O. C. Ward & Hadi Arbabi & Danielle Densley Tingley & Martin Mayfield, 2022. "Scalable Residential Building Geometry Characterisation Using Vehicle-Mounted Camera System," Energies, MDPI, vol. 15(16), pages 1-13, August.
    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. 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.
    12. 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).
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. 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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