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Spatiotemporal variability in building energy use in New York City

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  • Olivo, Y.
  • Hamidi, A.
  • Ramamurthy, P.

Abstract

Data on building energy use for large and dense cities is not yet available at adequate spatial and temporal scales. The energy consumption from buildings significantly influences the local climate and this impact is not adequately integrated into regional or local scale weather models. The primary objective of his study is to understand and map building energy consumption and quantify its impact on the urban environment; here, New York City (NYC) is used as a test case. The project involved a detailed classification of buildings in NYC using a high-resolution landuse/landcover dataset. The customized classification was then coupled with a single building energy model (SBEM) to estimate the building energy use. The developed model matched the annual energy use of NYC within 5% of the observed value. Coupled energy simulations were then performed with the Weather Research and Forecasting (WRF) model. The results show that heat released from building's heating and air conditioning system during extreme heat events can be as high as 18% of the overall available energy. Finally, a comparison between the average annual energy use, the urban heat island intensity (UHI) and the landcover/landuse fraction for various parcels during extreme heat events indicated that neighborhoods surrounding the highly-commercialized zones were disproportionately impacted by high UHI values. The increase was related to advection of heat.

Suggested Citation

  • Olivo, Y. & Hamidi, A. & Ramamurthy, P., 2017. "Spatiotemporal variability in building energy use in New York City," Energy, Elsevier, vol. 141(C), pages 1393-1401.
    • Handle: RePEc:eee:energy:v:141:y:2017:i:c:p:1393-1401
    DOI: 10.1016/j.energy.2017.11.066
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    References listed on IDEAS

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    1. Piet Eichholtz & Nils Kok & John M. Quigley, 2010. "Doing Well by Doing Good? Green Office Buildings," American Economic Review, American Economic Association, vol. 100(5), pages 2492-2509, December.
    2. Hou, Jing & Liu, Yisheng & Wu, Yong & Zhou, Nan & Feng, Wei, 2016. "Comparative study of commercial building energy-efficiency retrofit policies in four pilot cities in China," Energy Policy, Elsevier, vol. 88(C), pages 204-215.
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    Cited by:

    1. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Tuo, Junbo & Liu, Fei & Liu, Peiji & Zhang, Hua & Cai, Wei, 2018. "Energy efficiency evaluation for machining systems through virtual part," Energy, Elsevier, vol. 159(C), pages 172-183.
    3. Chen, Yibo & Wu, Jianzhong, 2018. "Distribution patterns of energy consumed in classified public buildings through the data mining process," Applied Energy, Elsevier, vol. 226(C), pages 240-251.
    4. Job Taminiau & John Byrne, 2020. "City‐scale urban sustainability: Spatiotemporal mapping of distributed solar power for New York City," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(5), September.
    5. 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.
    6. Wenliang Li, 2020. "Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data," Energies, MDPI, vol. 13(12), pages 1-22, June.

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