IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v244y2025ics0960148125003507.html
   My bibliography  Save this article

Assessment of BIPV power generation potential at the city scale based on local climate zones: Combining physical simulation, machine learning and 3D building models

Author

Listed:
  • Tang, Haida
  • Chai, Xingkang
  • Chen, Jiayu
  • Wan, Yang
  • Wang, Yuqin
  • Wan, Wei
  • Li, Chunying

Abstract

The adoption of distributed photovoltaic (PV) in cities can alleviate energy shortages, and building integrated photovoltaic (BIPV) has multiple advantages including building material saving and space saving. Predicting the potential of annual BIPV power generation (BIPVPG) and exploring the influencing factors in built environment is of great significance. This study created 50 simplified LCZ models based on the indicator ranges of LCZ categories 1–10. These models assume uniform building height and distribution to facilitate the simulation of annual BIPVPG. The annual BIPVPG (with PV materials applied on roofs and vertical facades) and the urban heat island (UHI) effects of the LCZ models in 15 cities were simulated. The UHI effects of the LCZ models were used to account for the reduction in BIPV efficiency caused by urban heat islands. Following that, the impact of climate and urban morphological factors on BIPVPG was studied using a multiple linear regression (MLR) and random forest (RF) model. According to the results, the RF model performed better in BIPV power generation prediction. Among urban morphological factors, average building height (ABH), aspect ratio (AR), and sky view factor (SVF) have dominant impact on urban BIPVPG. The impact on BIPVPG is minimal when ABH is between 10 and 15 m, and AR is between 1.5 and 2. When SVF is less than 0.8, the BIPVPG per unit building roof and wall area can be improved with building surface fraction (BSF) below 80. Among climate factors, solar radiation has significant impact on urban BIPVPG over other factors. Finally, based on the Guangzhou building data, the validated RF model was applied to predict Guangzhou's annual BIPV power potential. This study provides insights for machine learning models in BIPVPG assessment and offers quantitative recommendations for decision-makers and urban planners in developing BIPV cities with high energy resilience and sustainable level.

Suggested Citation

  • Tang, Haida & Chai, Xingkang & Chen, Jiayu & Wan, Yang & Wang, Yuqin & Wan, Wei & Li, Chunying, 2025. "Assessment of BIPV power generation potential at the city scale based on local climate zones: Combining physical simulation, machine learning and 3D building models," Renewable Energy, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:renene:v:244:y:2025:i:c:s0960148125003507
    DOI: 10.1016/j.renene.2025.122688
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125003507
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.122688?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. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2023. "Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches," Applied Energy, Elsevier, vol. 339(C).
    2. Pepermans, G. & Driesen, J. & Haeseldonckx, D. & Belmans, R. & D'haeseleer, W., 2005. "Distributed generation: definition, benefits and issues," Energy Policy, Elsevier, vol. 33(6), pages 787-798, April.
    3. 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).
    4. Lee, Kyung Sun & Lee, Jae Wook & Lee, Jae Seung, 2016. "Feasibility study on the relation between housing density and solar accessibility and potential uses," Renewable Energy, Elsevier, vol. 85(C), pages 749-758.
    5. Yadav, S. & Panda, S.K. & Tripathy, M., 2018. "Performance of building integrated photovoltaic thermal system with PV module installed at optimum tilt angle and influenced by shadow," Renewable Energy, Elsevier, vol. 127(C), pages 11-23.
    6. Kumar, Manish & Kumar, Arun, 2017. "Performance assessment and degradation analysis of solar photovoltaic technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 554-587.
    7. 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.
    8. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    9. 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.
    10. Li, Hao & Zhang, Ji & Liu, Xiaohua & Zhang, Tao, 2022. "Comparative investigation of energy-saving potential and technical economy of rooftop radiative cooling and photovoltaic systems," Applied Energy, Elsevier, vol. 328(C).
    11. Van Nguyen, Truong & Zhou, Li & Chong, Alain Yee Loong & Li, Boying & Pu, Xiaodie, 2020. "Predicting customer demand for remanufactured products: A data-mining approach," European Journal of Operational Research, Elsevier, vol. 281(3), pages 543-558.
    12. Miguel Centeno Brito & Paula Redweik & Cristina Catita & Sara Freitas & Miguel Santos, 2019. "3D Solar Potential in the Urban Environment: A Case Study in Lisbon," Energies, MDPI, vol. 12(18), pages 1-13, September.
    13. Arboit, M. & Diblasi, A. & Fernández Llano, J.C. & de Rosa, C., 2008. "Assessing the solar potential of low-density urban environments in Andean cities with desert climates: The case of the city of Mendoza, in Argentina," Renewable Energy, Elsevier, vol. 33(8), pages 1733-1748.
    14. Aixue Hu & Samuel Levis & Gerald A. Meehl & Weiqing Han & Warren M. Washington & Keith W. Oleson & Bas J. van Ruijven & Mingqiong He & Warren G. Strand, 2016. "Impact of solar panels on global climate," Nature Climate Change, Nature, vol. 6(3), pages 290-294, March.
    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. Drozd, Paweł & Kapica, Jacek & Jurasz, Jakub & Dąbek, Paweł, 2025. "Evaluating cities' solar potential using geographic information systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
    2. Yaoxuan Fang & Zheming Liu & Yingjie Jia & Muxuan Ke & Ruibing Yang & Yiyang Cai, 2025. "Impact of Urban Block Morphology on Solar Availability in Severe Cold High-Density Cities: A Case Study of Residential Blocks in Harbin," Land, MDPI, vol. 14(3), pages 1-35, March.
    3. Lv, Furong & Tang, Haiping, 2024. "Sustainable photovoltaic power generation spatial planning through ecosystem service valuation: A case study of the Qinghai-Tibet plateau," Renewable Energy, Elsevier, vol. 222(C).
    4. 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).
    5. Natanian, Jonathan & Aleksandrowicz, Or & Auer, Thomas, 2019. "A parametric approach to optimizing urban form, energy balance and environmental quality: The case of Mediterranean districts," Applied Energy, Elsevier, vol. 254(C).
    6. Shirazi, Ali Mohammad & Zomorodian, Zahra S. & Tahsildoost, Mohammad, 2019. "Techno-economic BIPV evaluation method in urban areas," Renewable Energy, Elsevier, vol. 143(C), pages 1235-1246.
    7. Liu, Jiang & Peng, Changhai & Zhang, Junxue, 2025. "Understanding the relationship between rural morphology and photovoltaic (PV) potential in traditional and non-traditional building clusters using shapley additive exPlanations (SHAP) values," Applied Energy, Elsevier, vol. 380(C).
    8. Li, Xiaochun & He, Ze & Xia, Siyou & Yang, Yu, 2024. "Greenness change associated with construction and operation of photovoltaic solar energy in China," Renewable Energy, Elsevier, vol. 226(C).
    9. Yanyan Huang & Yi Yang & Hangyi Ren & Lanxin Ye & Qinhan Liu, 2024. "From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance," Sustainability, MDPI, vol. 16(16), pages 1-27, August.
    10. 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).
    11. 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).
    12. Freitas, Jader de Sousa & Cronemberger, Joára & Soares, Raí Mariano & Amorim, Cláudia Naves David, 2020. "Modeling and assessing BIPV envelopes using parametric Rhinoceros plugins Grasshopper and Ladybug," Renewable Energy, Elsevier, vol. 160(C), pages 1468-1479.
    13. Tan, Hongjun & Guo, Zhiling & Chen, Yuntian & Zhang, Haoran & Song, Chenchen & Jiang, Mingkun & Yan, Jinyue, 2025. "PV potential analysis through deep learning and remote sensing-based urban land classification," Applied Energy, Elsevier, vol. 387(C).
    14. Bangjian Wang & Lihua Xu & Tianze Chen & Bowen Hou & Jianxun Liu & Yijun Shen & Rao Kuang, 2024. "High-Temperature Mechanical Properties of Basalt Fibers: A Step Towards Fire-Safe Materials for Photovoltaic Applications," Sustainability, MDPI, vol. 16(24), pages 1-13, December.
    15. 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).
    16. Pereira da Silva, Patrícia & Dantas, Guilherme & Pereira, Guillermo Ivan & Câmara, Lorrane & De Castro, Nivalde J., 2019. "Photovoltaic distributed generation – An international review on diffusion, support policies, and electricity sector regulatory adaptation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 30-39.
    17. 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.
    18. Funcke, Simon & Bauknecht, Dierk, 2016. "Typology of centralised and decentralised visions for electricity infrastructure," Utilities Policy, Elsevier, vol. 40(C), pages 67-74.
    19. Ruairi C. Robertson & Thaddeus J. Edens & Lynnea Carr & Kuda Mutasa & Ethan K. Gough & Ceri Evans & Hyun Min Geum & Iman Baharmand & Sandeep K. Gill & Robert Ntozini & Laura E. Smith & Bernard Chasekw, 2023. "The gut microbiome and early-life growth in a population with high prevalence of stunting," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    20. Blarke, Morten B., 2012. "Towards an intermittency-friendly energy system: Comparing electric boilers and heat pumps in distributed cogeneration," Applied Energy, Elsevier, vol. 91(1), pages 349-365.

    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:244:y:2025:i:c:s0960148125003507. 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.