IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v117y2016ip1p237-250.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216314918
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2016.10.057?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. 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.
    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. 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).
    2. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    3. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    4. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    5. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    6. Hanan S.S. Ibrahim & Ahmed Z. Khan & Shady Attia & Yehya Serag, 2021. "Classification of Heritage Residential Building Stock and Defining Sustainable Retrofitting Scenarios in Khedivial Cairo," Sustainability, MDPI, vol. 13(2), pages 1-26, January.
    7. 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.
    8. Brandão de Vasconcelos, Ana & Pinheiro, Manuel Duarte & Manso, Armando & Cabaço, António, 2015. "A Portuguese approach to define reference buildings for cost-optimal methodologies," Applied Energy, Elsevier, vol. 140(C), pages 316-328.
    9. Talebi, Behrang & Haghighat, Fariborz & Tuohy, Paul & Mirzaei, Parham A., 2018. "Validation of a community district energy system model using field measured data," Energy, Elsevier, vol. 144(C), pages 694-706.
    10. Kahouli, Sondès & Okushima, Shinichiro, 2021. "Regional energy poverty reevaluated: A direct measurement approach applied to France and Japan," Energy Economics, Elsevier, vol. 102(C).
    11. Tiziano Dalla Mora & Lorenzo Teso & Laura Carnieletto & Angelo Zarrella & Piercarlo Romagnoni, 2021. "Comparative Analysis between Dynamic and Quasi-Steady-State Methods at an Urban Scale on a Social-Housing District in Venice," Energies, MDPI, vol. 14(16), pages 1-22, August.
    12. Zhang, Xingxing & Lovati, Marco & Vigna, Ilaria & Widén, Joakim & Han, Mengjie & Gal, Csilla & Feng, Tao, 2018. "A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions," Applied Energy, Elsevier, vol. 230(C), pages 1034-1056.
    13. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    14. 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).
    15. Marquant, Julien F. & Evins, Ralph & Bollinger, L. Andrew & Carmeliet, Jan, 2017. "A holarchic approach for multi-scale distributed energy system optimisation," Applied Energy, Elsevier, vol. 208(C), pages 935-953.
    16. Kazas, Georgios & Fabrizio, Enrico & Perino, Marco, 2017. "Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study," Applied Energy, Elsevier, vol. 193(C), pages 243-262.
    17. Ilaria Ballarini & Vincenzo Corrado, 2017. "A New Methodology for Assessing the Energy Consumption of Building Stocks," Energies, MDPI, vol. 10(8), pages 1-22, July.
    18. Deller, David & Turner, Glen & Waddams Price, Catherine, 2021. "Energy poverty indicators: Inconsistencies, implications and where next?," Energy Economics, Elsevier, vol. 103(C).
    19. Alexandru Maxim & Costică Mihai & Constantin-Marius Apostoaie & Cristian Popescu & Costel Istrate & Ionel Bostan, 2016. "Implications and Measurement of Energy Poverty across the European Union," Sustainability, MDPI, vol. 8(5), pages 1-20, May.
    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:energy:v:117:y:2016:i:p1:p:237-250. 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/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.