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A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry

Author

Listed:
  • Diane Palmer

    (Centre for Renewable Energy Systems Technology (CREST), Loughborough University, Loughborough LE11 3TU, UK)

  • Elena Koumpli

    (Centre for Renewable Energy Systems Technology (CREST), Loughborough University, Loughborough LE11 3TU, UK
    Solar Century, London SE1 0NW, UK)

  • Ian Cole

    (Centre for Renewable Energy Systems Technology (CREST), Loughborough University, Loughborough LE11 3TU, UK
    FOSS Research Centre for Sustainable Energy, University of Cyprus (UCY), 1678 Nicosia, Cyprus)

  • Ralph Gottschalg

    (Fraunhofer Center for Silicon-Photovoltaic (CSP), 06120 Halle, Germany
    EMW, Hochschule Anhalt, 06366 Köthen, Germany)

  • Thomas Betts

    (Centre for Renewable Energy Systems Technology (CREST), Loughborough University, Loughborough LE11 3TU, UK)

Abstract

Knowledge of roof geometry and physical features is essential for evaluation of the impact of multiple rooftop solar photovoltaic (PV) system installations on local electricity networks. The paper starts by listing current methods used and stating their strengths and weaknesses. No current method is capable of delivering accurate results with publicly available input data. Hence a different approach is developed, based on slope and aspect using aircraft-based Light Detection and Ranging (LiDAR) data, building footprint data, GIS (Geographical Information Systems) tools, and aerial photographs. It assesses each roof’s suitability for PV deployment. That is, the characteristics of each roof are examined for fitting of at least a minimum size solar power system. In this way the minimum potential solar yield for region or city may be obtained. Accuracy is determined by ground-truthing against a database of 886 household systems. This is the largest validation of a rooftop assessment method to date. The method is flexible with few prior assumptions. It can generate data for various PV scenarios and future analyses.

Suggested Citation

  • Diane Palmer & Elena Koumpli & Ian Cole & Ralph Gottschalg & Thomas Betts, 2018. "A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry," Energies, MDPI, vol. 11(12), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3506-:d:190928
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    References listed on IDEAS

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    Cited by:

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    3. 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.
    4. Xiongyao Xie & Mingrui Zhao & Jiamin He & Biao Zhou, 2019. "Automatic and Visual Processing Method of Non-Contact Monitoring for Circular Stormwater Sewage Tunnels Based on LiDAR Data," Energies, MDPI, vol. 12(9), pages 1-19, April.
    5. Vytautas Bocullo & Linas Martišauskas & Darius Pupeikis & Ramūnas Gatautis & Rytis Venčaitis & Rimantas Bakas, 2023. "UAV Photogrammetry Application for Determining the Influence of Shading on Solar Photovoltaic Array Energy Efficiency," Energies, MDPI, vol. 16(3), pages 1-19, January.
    6. Myeongchan Oh & Hyeong-Dong Park, 2019. "Optimization of Solar Panel Orientation Considering Temporal Volatility and Scenario-Based Photovoltaic Potential: A Case Study in Seoul National University," Energies, MDPI, vol. 12(17), pages 1-17, August.
    7. 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).
    8. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
    9. 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.
    10. 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).
    11. 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.
    12. 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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