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Designing an Energy-Resilient Neighbourhood Using an Urban Building Energy Model

Author

Listed:
  • Niall Buckley

    (School of Geography, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Gerald Mills

    (School of Geography, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Samuel Letellier-Duchesne

    (Sustainable Design Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

  • Khadija Benis

    (Sustainable Design Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

Abstract

A climate resilient city, perforce, has an efficient and robust energy infrastructure that can harvest local energy resources and match energy sources and sinks that vary over space and time. This paper explores the use of an urban building energy model (UBEM) to examine the potential for creating a near-zero carbon neighbourhood in Dublin (Ireland) that is characterised by diverse land-uses and old and new building stock. UBEMs are a relatively new tool that allows the simulation of building energy demand across an urbanised landscape and can account for building layout, including the effects of overshadowing and the potential for facade retrofits and energy generation. In this research, a novel geographic database of buildings is created using archetypes, and the associated information on dimensions, fabric and energy systems is integrated into the Urban Modelling Interface (UMI). The model is used to simulate current and future energy demand based on climate change projections and to test scenarios that apply retrofits to the existing stock and that link proximate land-uses and land-covers. The latter allows a significant decoupling of the neighbourhood from an offsite electricity generation station with a high carbon output. The findings of this paper demonstrate that treating neighbourhoods as single energy entities rather than collections of individual sectors allows the development of bespoke carbon reducing scenarios that are geographically situated. The work shows the value of a neighbourhood-based approach to energy management using UBEMs.

Suggested Citation

  • Niall Buckley & Gerald Mills & Samuel Letellier-Duchesne & Khadija Benis, 2021. "Designing an Energy-Resilient Neighbourhood Using an Urban Building Energy Model," Energies, MDPI, vol. 14(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4445-:d:599816
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    References listed on IDEAS

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    1. Cerezo Davila, Carlos & Reinhart, Christoph F. & Bemis, Jamie L., 2016. "Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets," Energy, Elsevier, vol. 117(P1), pages 237-250.
    2. Yekini Suberu, Mohammed & Wazir Mustafa, Mohd & Bashir, Nouruddeen, 2014. "Energy storage systems for renewable energy power sector integration and mitigation of intermittency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 499-514.
    3. Ballarini, Ilaria & Corgnati, Stefano Paolo & Corrado, Vincenzo, 2014. "Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project," Energy Policy, Elsevier, vol. 68(C), pages 273-284.
    4. Fabbri, Kristian, 2015. "Building and fuel poverty, an index to measure fuel poverty: An Italian case study," Energy, Elsevier, vol. 89(C), pages 244-258.
    5. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
    6. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    7. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
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    Cited by:

    1. 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.
    2. Constantinos A. Balaras & Andreas I. Theodoropoulos & Elena G. Dascalaki, 2023. "Geographic Information Systems for Facilitating Audits of the Urban Built Environment," Energies, MDPI, vol. 16(11), pages 1-26, May.
    3. Avichal Malhotra & Simon Raming & Jérôme Frisch & Christoph van Treeck, 2021. "Open-Source Tool for Transforming CityGML Levels of Detail," Energies, MDPI, vol. 14(24), pages 1-26, December.
    4. Rui Liang & Xichuan Zheng & Jia Liang & Linhui Hu, 2023. "Energy Efficiency Model Construction of Building Carbon Neutrality Design," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    5. Ang, Yu Qian & Polly, Allison & Kulkarni, Aparna & Chambi, Gloria Bahl & Hernandez, Matthew & Haji, Maha N., 2022. "Multi-objective optimization of hybrid renewable energy systems with urban building energy modeling for a prototypical coastal community," Renewable Energy, Elsevier, vol. 201(P1), pages 72-84.

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