IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipds0306261924020993.html
   My bibliography  Save this article

Assessing building-integrated photovoltaic potential in dense urban areas using a multi-channel single-dimensional convolutional neural network model

Author

Listed:
  • Geng, Xiaotian
  • Cai, Senhong
  • Gou, Zhonghua

Abstract

Assessing BIPV (Building Integrated Photovoltaic) potential is of great significance for the comprehensive promotion and deployment of solar energy. Traditional models mostly rely on morphological parameters for PV potential assessment, presenting challenges such as subjective knowledge of urban forms and difficulty in generalization within dense urban areas. This study employs Convolutional Neural Network (CNN) for 3D modeling to evaluate BIPV potential at medium and large urban scales, introducing a framework for a multidimensional single-channel one-dimensional CNN model. The model utilizes the Gaussian Mixture Model combined with building point cloud data to extract the building window-to-wall ratio, thereby enhancing individual features in the building cluster point cloud. It also utilizes the 3D physical model to extract building geographic orientation information, integrating point cloud distribution through spatial connectivity to address the issue of missing geographic orientation due to rotational invariance of point cloud convolution. Additionally, it uses the surface area of the 3D model as the weight for surface point cloud sampling and combines it with normal estimation to retain building entity information, solving the disorder of point cloud convolution. This modeling framework enables accurate prediction of PV potentials in urban blocks by utilizing city point cloud data and predicting urban block boundaries. Using Melbourne City as a case study, the model demonstrates superior performance compared to traditional morphological parameter-based prediction models, with a root mean square error of 2415.548 kWh/year and an R2 SCORE of 0.937 in 75 training sets. The proposed modeling framework enables the prediction of multi-scale BIPV potential, which is beneficial for the staged promotion of BIPV and the development of effective energy deployment strategies. This study offers new insights for urban building energy modeling, deep learning, and energy prediction in complex scenarios at medium and large scales for sustainable urban development.

Suggested Citation

  • Geng, Xiaotian & Cai, Senhong & Gou, Zhonghua, 2025. "Assessing building-integrated photovoltaic potential in dense urban areas using a multi-channel single-dimensional convolutional neural network model," Applied Energy, Elsevier, vol. 377(PD).
  • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924020993
    DOI: 10.1016/j.apenergy.2024.124716
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924020993
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.124716?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. Hasan, Javeriya & Horvat, Miljana, 2023. "Spatial parameters and methodological approaches in solar potential assessment - State-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    2. 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).
    3. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    4. 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).
    5. 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).
    6. Sanaieian, Haniyeh & Tenpierik, Martin & Linden, Kees van den & Mehdizadeh Seraj, Fatemeh & Mofidi Shemrani, Seyed Majid, 2014. "Review of the impact of urban block form on thermal performance, solar access and ventilation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 551-560.
    7. Chen, Zhe & Yang, Bisheng & Zhu, Rui & Dong, Zhen, 2024. "City-scale solar PV potential estimation on 3D buildings using multi-source RS data: A case study in Wuhan, China," Applied Energy, Elsevier, vol. 359(C).
    8. Heo, Jae & Song, Kwonsik & Han, SangUk & Lee, Dong-Eun, 2021. "Multi-channel convolutional neural network for integration of meteorological and geographical features in solar power forecasting," Applied Energy, Elsevier, vol. 295(C).
    9. 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.
    10. Suomalainen, Kiti & Wang, Vincent & Sharp, Basil, 2017. "Rooftop solar potential based on LiDAR data: Bottom-up assessment at neighbourhood level," Renewable Energy, Elsevier, vol. 111(C), pages 463-475.
    11. Liu, Bo & Liu, Yu & Cho, Seigen & Chow, David Hou Chi, 2024. "Urban morphology indicators and solar radiation acquisition: 2011–2022 review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    12. Heo, Jae & Jung, Jaehoon & Kim, Byungil & Han, SangUk, 2020. "Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions," Applied Energy, Elsevier, vol. 262(C).
    13. Sarralde, Juan José & Quinn, David James & Wiesmann, Daniel & Steemers, Koen, 2015. "Solar energy and urban morphology: Scenarios for increasing the renewable energy potential of neighbourhoods in London," Renewable Energy, Elsevier, vol. 73(C), pages 10-17.
    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. Jurgis Zagorskas & Zenonas Turskis, 2025. "Performance Evaluation and Integration Strategies for Solar Façades in Diverse Climates: A State-of-the-Art Review," Sustainability, MDPI, vol. 17(3), pages 1-31, January.

    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. 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).
    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. 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).
    4. Qi, Qingqing & Zhao, Jinghao & Tan, Zekun & Tao, Kejun & Zhang, Xiaoqing & Tian, Yajun, 2024. "Development assessment of regional rooftop photovoltaics based on remote sensing and deep learning," Applied Energy, Elsevier, vol. 375(C).
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    10. 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).
    11. Zhang, Ji & Xu, Le & Shabunko, Veronika & Tay, Stephen En Rong & Sun, Huixuan & Lau, Stephen Siu Yu & Reindl, Thomas, 2019. "Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city," Applied Energy, Elsevier, vol. 240(C), pages 513-533.
    12. Jiang, Hou & Yao, Ling & Lu, Ning & Qin, Jun & Zhang, Xiaotong & Liu, Tang & Zhang, Xingxing & Zhou, Chenghu, 2024. "Exploring the optimization of rooftop photovoltaic scale and spatial layout under curtailment constraints," Energy, Elsevier, vol. 293(C).
    13. Mendis, Thushini & Huang, Zhaojian & Xu, Shen & Zhang, Weirong, 2020. "Economic potential analysis of photovoltaic integrated shading strategies on commercial building facades in urban blocks: A case study of Colombo, Sri Lanka," Energy, Elsevier, vol. 194(C).
    14. Guglielmina Mutani & Valeria Todeschi, 2021. "Optimization of Costs and Self-Sufficiency for Roof Integrated Photovoltaic Technologies on Residential Buildings," Energies, MDPI, vol. 14(13), pages 1-25, July.
    15. Zhang, Chen & Li, Zhixin & Jiang, Haihua & Luo, Yongqiang & Xu, Shen, 2021. "Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China," Applied Energy, Elsevier, vol. 283(C).
    16. Chen, Zhe & Yang, Bisheng & Zhu, Rui & Dong, Zhen, 2024. "City-scale solar PV potential estimation on 3D buildings using multi-source RS data: A case study in Wuhan, China," Applied Energy, Elsevier, vol. 359(C).
    17. Jiang, Wei & Zhang, Shuo & Wang, Teng & Zhang, Yufei & Sha, Aimin & Xiao, Jingjing & Yuan, Dongdong, 2024. "Evaluation method for the availability of solar energy resources in road areas before route corridor planning," Applied Energy, Elsevier, vol. 356(C).
    18. Zhu, Rui & Lau, Wing Sze & You, Linlin & Yan, Jinyue & Ratti, Carlo & Chen, Min & Wong, Man Sing & Qin, Zheng, 2024. "Multi-sourced data modelling of spatially heterogenous life-cycle carbon mitigation from installed rooftop photovoltaics: A case study in Singapore," Applied Energy, Elsevier, vol. 362(C).
    19. Nasrollahi, Nazanin & Shokri, Elham, 2016. "Daylight illuminance in urban environments for visual comfort and energy performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 861-874.
    20. Soares, N. & Bastos, J. & Pereira, L. Dias & Soares, A. & Amaral, A.R. & Asadi, E. & Rodrigues, E. & Lamas, F.B. & Monteiro, H. & Lopes, M.A.R. & Gaspar, A.R., 2017. "A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 845-860.

    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:377:y:2025:i:pd:s0306261924020993. 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.