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Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets

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  • Cerezo Davila, Carlos
  • Reinhart, Christoph F.
  • Bemis, Jamie L.

Abstract

City governments and energy utilities are increasingly focusing on the development of energy efficiency strategies for buildings as a key component in emission reduction plans and energy supply strategies. To support these diverse needs, a new generation of Urban Building Energy Models (UBEM) is currently being developed and validated to estimate citywide hourly energy demands at the building level. However, in order for cities to rely on UBEMs, effective model generation and maintenance workflows are needed based on existing urban data structures.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:117:y:2016:i:p1:p:237-250
    DOI: 10.1016/j.energy.2016.10.057
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    References listed on IDEAS

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    1. 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.
    2. Filogamo, Luana & Peri, Giorgia & Rizzo, Gianfranco & Giaccone, Antonino, 2014. "On the classification of large residential buildings stocks by sample typologies for energy planning purposes," Applied Energy, Elsevier, vol. 135(C), pages 825-835.
    3. Waddams Price, Catherine & Brazier, Karl & Wang, Wenjia, 2012. "Objective and subjective measures of fuel poverty," Energy Policy, Elsevier, vol. 49(C), pages 33-39.
    4. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    5. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    6. Dubois, Ute, 2012. "From targeting to implementation: The role of identification of fuel poor households," Energy Policy, Elsevier, vol. 49(C), pages 107-115.
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