IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v298y2021ics0306261921005729.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921005729
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117132?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. 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.
    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. Tian, Xinyi & Wang, Jun & Yuan, Shuang & Ji, Jie & Ke, Wei & Wang, Chuyao, 2023. "Investigation on the electrical performance of a curved PV roof integrated with CIGS cells for traditional Chinese houses," Energy, Elsevier, vol. 263(PC).
    2. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    3. Ding, Feng & Yang, Jianping & Zhou, Zan, 2023. "Economic profits and carbon reduction potential of photovoltaic power generation for China's high-speed railway infrastructure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    4. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    5. Chavid Leewiraphan & Nipon Ketjoy & Prapita Thanarak, 2023. "Business Perspectives of Distributed System Operators for Solar Rooftop-as-a-Service," Energies, MDPI, vol. 17(1), pages 1-15, December.
    6. Özdemir, Samed & Yavuzdoğan, Ahmet & Bilgilioğlu, Burhan Baha & Akbulut, Zeynep, 2023. "SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data," Renewable Energy, Elsevier, vol. 216(C).
    7. Wenbo Cui & Xiangang Peng & Jinhao Yang & Haoliang Yuan & Loi Lei Lai, 2023. "Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image," Energies, MDPI, vol. 16(18), pages 1-17, September.
    8. Zhixin Zhang & Min Chen & Teng Zhong & Rui Zhu & Zhen Qian & Fan Zhang & Yue Yang & Kai Zhang & Paolo Santi & Kaicun Wang & Yingxia Pu & Lixin Tian & Guonian Lü & Jinyue Yan, 2023. "Carbon mitigation potential afforded by rooftop photovoltaic in China," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    9. Sun, Yanwei & Li, Ying & Wang, Run & Ma, Renfeng, 2022. "Measuring dynamics of solar energy resource quality: Methodology and policy implications for reducing regional energy inequality," Renewable Energy, Elsevier, vol. 197(C), pages 138-150.
    10. Li, Yue & Luo, Hao & Cai, Hua, 2023. "Photovoltaic-battery powered bike share stations are not necessarily energy self-sufficient," Applied Energy, Elsevier, vol. 348(C).
    11. 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).
    12. Vera A. Barinova & Kseniya V. Demidova, 2023. "Экономическая Целесообразность Развития Солнечной Энергетики В России," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 10, pages 18-31, October.
    13. Yilin Xu & Jie He & Yang Liu & Zilu Li & Weicong Cai & Xiangang Peng, 2023. "Evaluation Method for Hosting Capacity of Rooftop Photovoltaic Considering Photovoltaic Potential in Distribution System," Energies, MDPI, vol. 16(22), pages 1-23, November.
    14. Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(C).
    15. Zheng, Jianan & Liu, Wenjun & Cui, Ting & Wang, Hanchun & Chen, Fangcai & Gao, Yang & Fan, Liulu & Ali Abaker Omer, Altyeb & Ingenhoff, Jan & Zhang, Xinyu & Liu, Wen, 2023. "A novel domino-like snow removal system for roof PV arrays: Feasibility, performance, and economic benefits," Applied Energy, Elsevier, vol. 333(C).
    16. Zhu, Rui & Kondor, Dániel & Cheng, Cheng & Zhang, Xiaohu & Santi, Paolo & Wong, Man Sing & Ratti, Carlo, 2022. "Solar photovoltaic generation for charging shared electric scooters," Applied Energy, Elsevier, vol. 313(C).
    17. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    18. Georgiou, Giorgos S. & Rouvas, Constantinos & Nathanael, Demetris, 2022. "Enhancing expansion of rooftop PV systems through Mixed Integer Linear Programming and Public Tender Procedures," Renewable Energy, Elsevier, vol. 187(C), pages 347-361.
    19. 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).
    20. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    21. 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).
    22. 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.
    23. Meng, B. & Loonen, R.C.G.M. & Hensen, J.L.M., 2022. "Performance variability and implications for yield prediction of rooftop PV systems – Analysis of 246 identical systems," Applied Energy, Elsevier, vol. 322(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. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    3. Bódis, Katalin & Kougias, Ioannis & Jäger-Waldau, Arnulf & Taylor, Nigel & Szabó, Sándor, 2019. "A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    4. Chen, Han & Chen, Wenying, 2021. "Status, trend, economic and environmental impacts of household solar photovoltaic development in China: Modelling from subnational perspective," Applied Energy, Elsevier, vol. 303(C).
    5. 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).
    6. Sebastian Krapf & Nils Kemmerzell & Syed Khawaja Haseeb Uddin & Manuel Hack Vázquez & Fabian Netzler & Markus Lienkamp, 2021. "Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning," Energies, MDPI, vol. 14(13), pages 1-22, June.
    7. 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.
    8. Sánchez-Aparicio, M. & Martín-Jiménez, J. & Del Pozo, S. & González-González, E. & Lagüela, S., 2021. "Ener3DMap-SolarWeb roofs: A geospatial web-based platform to compute photovoltaic potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Yang, Ying & Campana, Pietro Elia & Yan, Jinyue, 2020. "Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Tu, Qiang & Mo, Jianlei & Betz, Regina & Cui, Lianbiao & Fan, Ying & Liu, Yu, 2020. "Achieving grid parity of solar PV power in China- The role of Tradable Green Certificate," Energy Policy, Elsevier, vol. 144(C).
    11. Liao, Xuan & Zhu, Rui & Wong, Man Sing & Heo, Joon & Chan, P.W. & Kwok, Coco Yin Tung, 2023. "Fast and accurate estimation of solar irradiation on building rooftops in Hong Kong: A machine learning-based parameterization approach," Renewable Energy, Elsevier, vol. 216(C).
    12. 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).
    13. Zhong, Teng & Zhang, Kai & Chen, Min & Wang, Yijie & Zhu, Rui & Zhang, Zhixin & Zhou, Zixuan & Qian, Zhen & Lv, Guonian & Yan, Jinyue, 2021. "Assessment of solar photovoltaic potentials on urban noise barriers using street-view imagery," Renewable Energy, Elsevier, vol. 168(C), pages 181-194.
    14. Li, Jianglong & Huang, Jiashun, 2020. "The expansion of China's solar energy: Challenges and policy options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    15. Zhixin Li & Chen Zhang & Zejun Yu & Hong Zhang & Haihua Jiang, 2023. "Deep Learning Method for Evaluating Photovoltaic Potential of Rural Land Use Types," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    16. 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).
    17. Lee, Minhyun & Hong, Taehoon & Jeong, Kwangbok & Kim, Jimin, 2018. "A bottom-up approach for estimating the economic potential of the rooftop solar photovoltaic system considering the spatial and temporal diversity," Applied Energy, Elsevier, vol. 232(C), pages 640-656.
    18. Wang, Yu & He, Jijiang & Chen, Wenying, 2021. "Distributed solar photovoltaic development potential and a roadmap at the city level in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    19. Gomez-Exposito, Antonio & Arcos-Vargas, Angel & Gutierrez-Garcia, Francisco, 2020. "On the potential contribution of rooftop PV to a sustainable electricity mix: The case of Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    20. Libo Zhang & Qian Du & Dequn Zhou, 2021. "Grid Parity Analysis of China’s Centralized Photovoltaic Generation under Multiple Uncertainties," Energies, MDPI, vol. 14(7), pages 1-19, March.

    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:298:y:2021:i:c:s0306261921005729. 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.