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An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images

Author

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  • Xiaoyang Song

    (Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China)

  • Yaohuan Huang

    (Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Chuanpeng Zhao

    (Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yuxin Liu

    (Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    Key Laboratory of Petroleum Resource Research, Institute of Geology and Geophysics, Chinese Academy of Science, Beijing 100029, China
    Institute of Earth Science, Chinese Academy of Science, Beijing 100029, China)

  • Yanguo Lu

    (Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China)

  • Yongguo Chang

    (Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China)

  • Jie Yang

    (Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

Solar energy is the most clean renewable energy source and has good prospects for future sustainable development. Installation of solar photovoltaic (PV) systems on building rooftops has been the most widely applied method for using solar energy resources. In this study, we developed an approach to simulate the monthly and annual solar radiation on rooftops at an hourly time step to estimate the solar PV potential, based on rooftop feature retrieval from remote sensing images. The rooftop features included 2D rooftop outlines and 3D rooftop parameters retrieved from high-resolution remote sensing image data (obtained from Google Maps) and digital surface model (DSM, generated from the Pleiades satellite), respectively. We developed the building features calculation method for five rooftop types: flat rooftops, shed rooftops, hipped rooftops, gable rooftops and mansard rooftops. The parameters of the PV modules derived from the building features were then combined with solar radiation data to evaluate solar photovoltaic potential. The proposed method was applied in the Chao Yang District of Beijing, China. The results were that the number of rooftops available for PV systems was 743, the available rooftop area was 678,805 m 2 , and the annual PV electricity potential was 63.78 GWh/year in the study area, which has great solar PV potential. The method to perform precise calculation of specific rooftop solar PV potential developed in this study will guide the formulation of energy policy for solar PV in the future.

Suggested Citation

  • Xiaoyang Song & Yaohuan Huang & Chuanpeng Zhao & Yuxin Liu & Yanguo Lu & Yongguo Chang & Jie Yang, 2018. "An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images," Energies, MDPI, vol. 11(11), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3172-:d:183168
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    References listed on IDEAS

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    9. Yildirim, Deniz & Büyüksalih, Gürcan & Şahin, Ahmet Duran, 2021. "Rooftop photovoltaic potential in Istanbul: Calculations based on LiDAR data, measurements and verifications," Applied Energy, Elsevier, vol. 304(C).
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    12. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    13. Jiandong Liu & Yanbo Shen & Guangsheng Zhou & De-Li Liu & Qiang Yu & Jun Du, 2023. "Calibrating the Ångström–Prescott Model with Solar Radiation Data Collected over Long and Short Periods of Time over the Tibetan Plateau," Energies, MDPI, vol. 16(20), pages 1-16, October.
    14. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).
    15. 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.
    16. 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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