IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4400-d1470091.html
   My bibliography  Save this article

Integrating Occupant Behaviour into Urban-Building Energy Modelling: A Review of Current Practices and Challenges

Author

Listed:
  • Alessia Banfi

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Martina Ferrando

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Peixian Li

    (College of Architecture and Urban Planning, Tongji University, No. 1239 Si Ping Road, Shanghai 200092, China)

  • Xing Shi

    (College of Architecture and Urban Planning, Tongji University, No. 1239 Si Ping Road, Shanghai 200092, China)

  • Francesco Causone

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

Abstract

Urban-Building Energy Modelling (UBEM) tools play a crucial role in analysing and optimizing energy use within cities. Among the available approaches, the bottom-up physics-based one is the most versatile for urban development and management applications. However, their accuracy is often limited by the inability to capture the dynamic impact of occupants’ presence and actions (i.e., Occupant Behaviour) on building energy use patterns. While recent research has explored advanced Occupant Behaviour (OB) modelling techniques that incorporate stochasticity and contextual influences, current UBEM practices primarily rely on static occupant profiles, due to limitations in the software itself. This paper addresses this topic by conducting a thorough literature review to examine existing OB modelling techniques, data sources, key features and detailed information that could enhance UBEM simulations. Furthermore, the flexibility of available UBEM tools for integrating advanced OB models will be assessed, along with the identification of areas for improvement. The findings of this review are intended to guide researchers and tool developers towards creating more robust and occupant-centric urban energy simulations.

Suggested Citation

  • Alessia Banfi & Martina Ferrando & Peixian Li & Xing Shi & Francesco Causone, 2024. "Integrating Occupant Behaviour into Urban-Building Energy Modelling: A Review of Current Practices and Challenges," Energies, MDPI, vol. 17(17), pages 1-28, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4400-:d:1470091
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4400/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/17/4400/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. An, Jingjing & Yan, Da & Hong, Tianzhen & Sun, Kaiyu, 2017. "A novel stochastic modeling method to simulate cooling loads in residential districts," Applied Energy, Elsevier, vol. 206(C), pages 134-149.
    2. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    3. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
    4. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    5. Chen, Jianli & Adhikari, Rajendra & Wilson, Eric & Robertson, Joseph & Fontanini, Anthony & Polly, Ben & Olawale, Opeoluwa, 2022. "Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model," Applied Energy, Elsevier, vol. 325(C).
    6. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    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. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).
    2. Javanroodi, Kavan & Mahdavinejad, Mohammadjavad & Nik, Vahid M., 2018. "Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate," Applied Energy, Elsevier, vol. 231(C), pages 714-746.
    3. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    4. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    5. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    6. Ehsan Kamel, 2022. "A Systematic Literature Review of Physics-Based Urban Building Energy Modeling (UBEM) Tools, Data Sources, and Challenges for Energy Conservation," Energies, MDPI, vol. 15(22), pages 1-24, November.
    7. Xu, Xiaoxiao & Yu, Hao & Sun, Qiuwen & Tam, Vivian W.Y., 2023. "A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    8. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    9. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    10. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    11. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    12. Younghun Choi & Takuro Kobashi & Yoshiki Yamagata & Akito Murayama, 2021. "Assessment of waterfront office redevelopment plan on optimal building energy demand and rooftop photovoltaics for urban decarbonization," Papers 2108.09029, arXiv.org.
    13. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    14. Haleh Moghaddasi & Charles Culp & Jorge Vanegas & Saptarshi Das & Mehrdad Ehsani, 2022. "An Adaptable Net Zero Model: Energy Analysis of a Monitored Case Study," Energies, MDPI, vol. 15(11), pages 1-24, May.
    15. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    16. Axel Bruck & Luca Casamassima & Ardak Akhatova & Lukas Kranzl & Kostas Galanakis, 2022. "Creating Comparability among European Neighbourhoods to Enable the Transition of District Energy Infrastructures towards Positive Energy Districts," Energies, MDPI, vol. 15(13), pages 1-21, June.
    17. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    18. Guglielmina Mutani & Valeria Todeschi & Simone Beltramino, 2020. "Energy Consumption Models at Urban Scale to Measure Energy Resilience," Sustainability, MDPI, vol. 12(14), pages 1-31, July.
    19. Katal, Ali & Mortezazadeh, Mohammad & Wang, Liangzhu (Leon), 2019. "Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations," Applied Energy, Elsevier, vol. 250(C), pages 1402-1417.
    20. Edmund Widl & Giorgio Agugiaro & Jan Peters-Anders, 2021. "Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level," Sustainability, MDPI, vol. 13(16), pages 1-24, August.

    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:gam:jeners:v:17:y:2024:i:17:p:4400-:d:1470091. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.