IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v148y2015icp93-104.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915002974
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.03.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Goe, Michele & Gaustad, Gabrielle, 2014. "Strengthening the case for recycling photovoltaics: An energy payback analysis," Applied Energy, Elsevier, vol. 120(C), pages 41-48.
    2. Bull, Joe, 2012. "Loads of green washing—can behavioural economics increase willingness-to-pay for efficient washing machines in the UK?," Energy Policy, Elsevier, vol. 50(C), pages 242-252.
    3. Chen, Hsing Hung & Lee, Amy H.I. & Chen, Silu, 2014. "Strategic policy to select suitable intermediaries for innovation to promote PV solar energy industry in China," Applied Energy, Elsevier, vol. 115(C), pages 429-437.
    4. Lukač, Niko & Žlaus, Danijel & Seme, Sebastijan & Žalik, Borut & Štumberger, Gorazd, 2013. "Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data," Applied Energy, Elsevier, vol. 102(C), pages 803-812.
    5. Kucuksari, Sadik & Khaleghi, Amirreza M. & Hamidi, Maryam & Zhang, Ye & Szidarovszky, Ferenc & Bayraksan, Guzin & Son, Young-Jun, 2014. "An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments," Applied Energy, Elsevier, vol. 113(C), pages 1601-1613.
    6. Sauter, Raphael & Watson, Jim, 2007. "Strategies for the deployment of micro-generation: Implications for social acceptance," Energy Policy, Elsevier, vol. 35(5), pages 2770-2779, May.
    7. 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.
    8. Hong, Taehoon & Koo, Choongwan & Park, Joonho & Park, Hyo Seon, 2014. "A GIS (geographic information system)-based optimization model for estimating the electricity generation of the rooftop PV (photovoltaic) system," Energy, Elsevier, vol. 65(C), pages 190-199.
    9. Cherrington, R. & Goodship, V. & Longfield, A. & Kirwan, K., 2013. "The feed-in tariff in the UK: A case study focus on domestic photovoltaic systems," Renewable Energy, Elsevier, vol. 50(C), pages 421-426.
    10. Muhammad-Sukki, Firdaus & Ramirez-Iniguez, Roberto & Munir, Abu Bakar & Mohd Yasin, Siti Hajar & Abu-Bakar, Siti Hawa & McMeekin, Scott G. & Stewart, Brian G., 2013. "Revised feed-in tariff for solar photovoltaic in the United Kingdom: A cloudy future ahead?," Energy Policy, Elsevier, vol. 52(C), pages 832-838.
    11. Bush, Ruth & Jacques, David A. & Scott, Kate & Barrett, John, 2014. "The carbon payback of micro-generation: An integrated hybrid input–output approach," Applied Energy, Elsevier, vol. 119(C), pages 85-98.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. Lingfors, D. & Bright, J.M. & Engerer, N.A. & Ahlberg, J. & Killinger, S. & Widén, J., 2017. "Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis," Applied Energy, Elsevier, vol. 205(C), pages 1216-1230.
    3. 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.
    4. Huang, Zhaojian & Mendis, Thushini & Xu, Shen, 2019. "Urban solar utilization potential mapping via deep learning technology: A case study of Wuhan, China," Applied Energy, Elsevier, vol. 250(C), pages 283-291.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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).
    10. 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.
    11. 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).
    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. 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.
    14. 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.
    15. 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).
    16. 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.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Allan, Grant & Eromenko, Igor & Gilmartin, Michelle & Kockar, Ivana & McGregor, Peter, 2015. "The economics of distributed energy generation: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 543-556.
    3. Hong, Taehoon & Lee, Minhyun & Koo, Choongwan & Jeong, Kwangbok & Kim, Jimin, 2017. "Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis," Applied Energy, Elsevier, vol. 194(C), pages 320-332.
    4. 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.
    5. Abu-Bakar, Siti Hawa & Muhammad-Sukki, Firdaus & Ramirez-Iniguez, Roberto & Mallick, Tapas Kumar & McLennan, Campbell & Munir, Abu Bakar & Mohd Yasin, Siti Hajar & Abdul Rahim, Ruzairi, 2013. "Is Renewable Heat Incentive the future?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 365-378.
    6. Paul Westacott & Chiara Candelise, 2016. "A Novel Geographical Information Systems Framework to Characterize Photovoltaic Deployment in the UK: Initial Evidence," Energies, MDPI, vol. 9(1), pages 1-20, January.
    7. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    8. Shafiullah, Md & Rahman, Syed Masiur & Mortoja, Md. Golam & Al-Ramadan, Baqer, 2016. "Role of spatial analysis technology in power system industry: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 584-595.
    9. 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.
    10. Bossavy, Arthur & Girard, Robin & Kariniotakis, Georges, 2016. "Sensitivity analysis in the technical potential assessment of onshore wind and ground solar photovoltaic power resources at regional scale," Applied Energy, Elsevier, vol. 182(C), pages 145-153.
    11. 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.
    12. Simpson, Genevieve & Clifton, Julian, 2016. "Subsidies for residential solar photovoltaic energy systems in Western Australia: Distributional, procedural and outcome justice," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 262-273.
    13. Hanna, Richard & Leach, Matthew & Torriti, Jacopo, 2018. "Microgeneration: The installer perspective," Renewable Energy, Elsevier, vol. 116(PA), pages 458-469.
    14. 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).
    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. Allan, Grant J. & McIntyre, Stuart G., 2017. "Green in the heart or greens in the wallet? The spatial uptake of small-scale renewable technologies," Energy Policy, Elsevier, vol. 102(C), pages 108-115.
    17. 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.
    18. Sarawut Ninsawat & Mohammad Dalower Hossain, 2016. "Identifying Potential Area and Financial Prospects of Rooftop Solar Photovoltaics (PV)," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    19. 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.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:148:y:2015:i:c:p:93-104. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.