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A city-scale estimation of rooftop solar photovoltaic potential based on deep learning

Author

Listed:
  • Zhong, Teng
  • Zhang, Zhixin
  • Chen, Min
  • Zhang, Kai
  • Zhou, Zixuan
  • Zhu, Rui
  • Wang, Yijie
  • Lü, Guonian
  • Yan, Jinyue

Abstract

The estimation of rooftop solar photovoltaic (PV) potential is crucial for policymaking around sustainable energy plans. But it is difficult to accurately estimate the availability of rooftop area for solar radiation on a city-scale. In this study, a generic framework for estimating the rooftop solar PV potential on a city-scale using publicly available high-resolution satellite images is proposed. A deep learning-based method is developed to extract the rooftop area with image semantic segmentation automatically. A spatial optimization sampling strategy is developed to solve the labor-intensive problem when training the rooftop extraction model based on prior knowledge of urban and rural spatial layout and land use. In the case study of Nanjing, China, the labor cost on preparing the dataset for training the rooftop extraction model has been reduced by about 80% with the proposed spatial optimization sampling strategy. Meanwhile, the robustness of the rooftop extraction model in districts with different architectural styles and land use has been improved. The total rooftop area extracted was 330.36 km2, and the overall accuracy reached 0.92. The estimation results show that Nanjing has significant potential for rooftop-mounted PV installations, and the potential installed capacity reached 66 GW. The annual rooftop solar PV potential was approximately 311,853 GWh, with a corresponding estimated power generation of 49,897 GWh in 2019.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921005729
    DOI: 10.1016/j.apenergy.2021.117132
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    References listed on IDEAS

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    1. Jinyue Yan & Ying Yang & Pietro Elia Campana & Jijiang He, 2019. "City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China," Nature Energy, Nature, vol. 4(8), pages 709-717, August.
    2. Freitas, S. & Catita, C. & Redweik, P. & Brito, M.C., 2015. "Modelling solar potential in the urban environment: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 915-931.
    3. Brito, M.C. & Freitas, S. & Guimarães, S. & Catita, C. & Redweik, P., 2017. "The importance of facades for the solar PV potential of a Mediterranean city using LiDAR data," Renewable Energy, Elsevier, vol. 111(C), pages 85-94.
    4. 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.
    5. Sun, Honghang & Zhi, Qiang & Wang, Yibo & Yao, Qiang & Su, Jun, 2014. "China’s solar photovoltaic industry development: The status quo, problems and approaches," Applied Energy, Elsevier, vol. 118(C), pages 221-230.
    6. Xu, Mei & Xie, Pu & Xie, Bai-Chen, 2020. "Study of China's optimal solar photovoltaic power development path to 2050," Resources Policy, Elsevier, vol. 65(C).
    7. Zhang, Yuhu & Ren, Jing & Pu, Yanru & Wang, Peng, 2020. "Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis," Renewable Energy, Elsevier, vol. 149(C), pages 577-586.
    8. 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.
    9. Thorvaldur Gylfason, 2020. "Africa’s Growth Prospects are Good," Challenge, Taylor & Francis Journals, vol. 63(2), pages 98-106, March.
    10. 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.
    11. Zou, Hongyang & Du, Huibin & Ren, Jingzheng & Sovacool, Benjamin K. & Zhang, Yongjie & Mao, Guozhu, 2017. "Market dynamics, innovation, and transition in China's solar photovoltaic (PV) industry: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 197-206.
    12. Rigter, Jasper & Vidican, Georgeta, 2010. "Cost and optimal feed-in tariff for small scale photovoltaic systems in China," Energy Policy, Elsevier, vol. 38(11), pages 6989-7000, November.
    13. Berwal, Anil K. & Kumar, Sanjay & Kumari, Nisha & Kumar, Virender & Haleem, Abid, 2017. "Design and analysis of rooftop grid tied 50kW capacity Solar Photovoltaic (SPV) power plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1288-1299.
    14. Ko, Li & Wang, Jen-Chun & Chen, Chia-Yon & Tsai, Hsing-Yeh, 2015. "Evaluation of the development potential of rooftop solar photovoltaic in Taiwan," Renewable Energy, Elsevier, vol. 76(C), pages 582-595.
    15. 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.
    16. Xin-gang, Zhao & Zhen, Wang, 2019. "Technology, cost, economic performance of distributed photovoltaic industry in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 53-64.
    17. Kabir, Md. Humayun & Endlicher, Wilfried & Jägermeyr, Jonas, 2010. "Calculation of bright roof-tops for solar PV applications in Dhaka Megacity, Bangladesh," Renewable Energy, Elsevier, vol. 35(8), pages 1760-1764.
    18. Tyagi, V.V. & Rahim, Nurul A.A. & Rahim, N.A. & Selvaraj, Jeyraj A./L., 2013. "Progress in solar PV technology: Research and achievement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 443-461.
    19. Duan, Hong-Bo & Zhang, Gu-Peng & Zhu, Lei & Fan, Ying & Wang, Shou-Yang, 2016. "How will diffusion of PV solar contribute to China׳s emissions-peaking and climate responses?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1076-1085.
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