IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v237y2024ipcs0960148124018780.html
   My bibliography  Save this article

A novel method for assessment rooftop PV potential based on remote sensing images

Author

Listed:
  • Yang, Jinhao
  • Wu, Jinghua
  • Lu, Junjie
  • Peng, Xiangang
  • Yuan, Haoliang
  • Lai, Loi Lei

Abstract

The assessment of rooftop photovoltaic (PV) potential is highly significant for energy policy formulation. With the rapid development of computer vision (CV) and remote sensing imagery, utilizing CV to extract rooftop information is an ideal approach. However, deep learning requires a large amount of accurately annotated data, and annotating remote sensing images is a labor-intensive task. This limitation hinders the application of deep learning in rooftop PV potential assessment. To address this issue, this paper proposes a semi-supervised learning (SSL)-based segmentation model to extract rooftop information from remote sensing images. Subsequently, a rooftop classification method is proposed to categorize rooftops into several classes and estimate their rooftop PV available area ratios. Finally, the total available rooftop PV area in urban areas is evaluated, and the potential rooftop PV installed capacity and power generation are calculated. This method is applied in the Longhu District of Shantou City, Guangdong Province. The evaluation results show that the total rooftop area in Longhu District is 17.2 km2, with a rooftop PV available area of 12.7 km2. It is estimated that the rooftop PV installed capacity in Longhu District is 1849.4 MW, with an annual power generation of 2219.3 GWh.

Suggested Citation

  • Yang, Jinhao & Wu, Jinghua & Lu, Junjie & Peng, Xiangang & Yuan, Haoliang & Lai, Loi Lei, 2024. "A novel method for assessment rooftop PV potential based on remote sensing images," Renewable Energy, Elsevier, vol. 237(PC).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pc:s0960148124018780
    DOI: 10.1016/j.renene.2024.121810
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124018780
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.121810?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. Pan, Keda & Chen, Zhaohua & Lai, Chun Sing & Xie, Changhong & Wang, Dongxiao & Li, Xuecong & Zhao, Zhuoli & Tong, Ning & Lai, Loi Lei, 2022. "An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation," Applied Energy, Elsevier, vol. 309(C).
    2. 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).
    3. Haas, Reinhard & Duic, Neven & Auer, Hans & Ajanovic, Amela & Ramsebner, Jasmine & Knapek, Jaroslav & Zwickl-Bernhard, Sebastian, 2023. "The photovoltaic revolution is on: How it will change the electricity system in a lasting way," Energy, Elsevier, vol. 265(C).
    4. 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).
    5. Peng, Jinqing & Lu, Lin, 2013. "Investigation on the development potential of rooftop PV system in Hong Kong and its environmental benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 149-162.
    6. Tang, Ningning & Zhang, Yuning & Niu, Yuguang & Du, Xiaoze, 2018. "Solar energy curtailment in China: Status quo, reasons and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 509-528.
    7. Gabriele Lobaccaro & Malgorzata Maria Lisowska & Erika Saretta & Pierluigi Bonomo & Francesco Frontini, 2019. "A Methodological Analysis Approach to Assess Solar Energy Potential at the Neighborhood Scale," Energies, MDPI, vol. 12(18), pages 1-28, September.
    8. 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).
    9. Orioli, Aldo & Di Gangi, Alessandra, 2015. "The recent change in the Italian policies for photovoltaics: Effects on the payback period and levelized cost of electricity of grid-connected photovoltaic systems installed in urban contexts," Energy, Elsevier, vol. 93(P2), pages 1989-2005.
    10. 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).
    11. Kovač, Marko & Stegnar, Gašper & Al-Mansour, Fouad & Merše, Stane & Pečjak, Andrej, 2019. "Assessing solar potential and battery instalment for self-sufficient buildings with simplified model," Energy, Elsevier, vol. 173(C), pages 1182-1195.
    12. 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).
    13. Kutlu, Elif Ceren & Durusoy, Beyza & Ozden, Talat & Akinoglu, Bulent G., 2022. "Technical potential of rooftop solar photovoltaic for Ankara," Renewable Energy, Elsevier, vol. 185(C), pages 779-789.
    14. Tian, Shuai & Yang, Guoqiang & Du, Sihong & Zhuang, Dian & Zhu, Ke & Zhou, Xin & Jin, Xing & Ye, Yu & Li, Peixian & Shi, Xing, 2024. "An innovative method for evaluating the urban roof photovoltaic potential based on open-source satellite images," Renewable Energy, Elsevier, vol. 224(C).
    15. Shahsavari, Amir & Akbari, Morteza, 2018. "Potential of solar energy in developing countries for reducing energy-related emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 275-291.
    16. Wang, Chaofan & Wang, Yilan & Zhao, Yujia & Shuai, Jing & Shuai, Chuanmin & Cheng, Xin, 2023. "Cognition process and influencing factors of rural residents' adoption willingness for solar PV poverty alleviation projects: Evidence from a mixed methodology in rural China," Energy, Elsevier, vol. 271(C).
    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).
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Tian, Shuai & Yang, Guoqiang & Du, Sihong & Zhuang, Dian & Zhu, Ke & Zhou, Xin & Jin, Xing & Ye, Yu & Li, Peixian & Shi, Xing, 2024. "An innovative method for evaluating the urban roof photovoltaic potential based on open-source satellite images," Renewable Energy, Elsevier, vol. 224(C).
    3. Li, Qingyu & Krapf, Sebastian & Mou, Lichao & Shi, Yilei & Zhu, Xiao Xiang, 2024. "Deep learning-based framework for city-scale rooftop solar potential estimation by considering roof superstructures," Applied Energy, Elsevier, vol. 374(C).
    4. Liang, Hanwei & Shen, Jieling & Yip, Hin-Lap & Fang, Mandy Meng & Dong, Liang, 2024. "Unleashing the green potential: Assessing Hong Kong's building solar PV capacity," Applied Energy, Elsevier, vol. 369(C).
    5. Du, Hua & Han, Qi & de Vries, Bauke & Sun, Jun, 2024. "Community solar PV adoption in residential apartment buildings: A case study on influencing factors and incentive measures in Wuhan," Applied Energy, Elsevier, vol. 354(PA).
    6. Aotian Song & Lin Lu & Zhizhao Liu & Man Sing Wong, 2016. "A Study of Incentive Policies for Building-Integrated Photovoltaic Technology in Hong Kong," Sustainability, MDPI, vol. 8(8), pages 1-21, August.
    7. Thebault, Martin & Desthieux, Gilles & Castello, Roberto & Berrah, Lamia, 2022. "Large-scale evaluation of the suitability of buildings for photovoltaic integration: Case study in Greater Geneva," Applied Energy, Elsevier, vol. 316(C).
    8. 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).
    9. 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).
    10. 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.
    11. Ren, Haoshan & Ma, Zhenjun & Ming Lun Fong, Alan & Sun, Yongjun, 2022. "Optimal deployment of distributed rooftop photovoltaic systems and batteries for achieving net-zero energy of electric bus transportation in high-density cities," Applied Energy, Elsevier, vol. 319(C).
    12. Wei, Tianxi & Zhang, Yi & Zhang, Yuhang & Miao, Rui & Kang, Jian & Qi, He, 2024. "City-scale roof-top photovoltaic deployment planning," Applied Energy, Elsevier, vol. 368(C).
    13. Žalik, Mitja & Mongus, Domen & Lukač, Niko, 2024. "High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning," Renewable Energy, Elsevier, vol. 222(C).
    14. Salim, Daniel Henrique Carneiro & de Sousa Mello, Caio César & Franco, Guilherme Gandra & de Albuquerque Nóbrega, Rodrigo Affonso & de Paula, Eduardo Coutinho & Fonseca, Bráulio Magalhães & Nero, Marc, 2023. "Unveiling Fernando de Noronha Island's photovoltaic potential with unmanned aerial survey and irradiation modeling," Applied Energy, Elsevier, vol. 337(C).
    15. Lodhi, Muhammad Kamran & Tan, Yumin & Wang, Xiaolu & Masum, Syed Muhammad & Nouman, Khan Muhammad & Ullah, Nasim, 2024. "Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach," Energy, Elsevier, vol. 304(C).
    16. Song, Zhiying & Ji, Jie & Cai, Jingyong & Zhao, Bin & Li, Zhaomeng, 2021. "Investigation on a direct-expansion solar-assisted heat pump with a novel hybrid compound parabolic concentrator/photovoltaic/fin evaporator," Applied Energy, Elsevier, vol. 299(C).
    17. Song, Zhiying & Ji, Jie & Zhang, Yuzhe & Cai, Jingyong & Li, Zhaomeng & Li, Yunhai, 2023. "Mathematical and experimental investigation about the dual-source heat pump integrating low concentrated photovoltaic and finned-tube exchanger," Energy, Elsevier, vol. 263(PE).
    18. Qian, Yuan & Scherer, Laura & Tukker, Arnold & Behrens, Paul, 2020. "China's potential SO2 emissions from coal by 2050," Energy Policy, Elsevier, vol. 147(C).
    19. Formolli, M. & Kleiven, T. & Lobaccaro, G., 2023. "Assessing solar energy accessibility at high latitudes: A systematic review of urban spatial domains, metrics, and parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    20. Hao Cai & Ling Liang & Jing Tang & Qianxian Wang & Lihong Wei & Jiaping Xie, 2019. "An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors," Sustainability, MDPI, vol. 11(23), pages 1-22, 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:renene:v:237:y:2024:i:pc:s0960148124018780. 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.journals.elsevier.com/renewable-energy .

    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.