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Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations

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  • Katal, Ali
  • Mortezazadeh, Mohammad
  • Wang, Liangzhu (Leon)

Abstract

In the past decade, urban building energy models have been developed to address the increasing concerns over energy consumption and greenhouse gas emission due to rapid urbanization and building resilience as a result of climate change. These models can estimate energy consumption, GHG emission and resilience response of buildings in an urban area, and evaluate retrofit strategies for architects, engineers, researchers and policy makers. It has been recognized that local microclimate and neighborhood effects play an important role in urban building energy modeling. Creating an urban building energy model also requires the collection of extensive building data, which is a time-consuming process. In this study, we developed an integrated platform by combining CityFFD (City Fast Fluid Dynamics), an urban-scale fast fluid dynamics model for microclimate modeling, and CityBEM (City Building Energy Model), a new urban building energy model with a library of 1700 building archetypes for facilitating urban model creation. Local aerodynamics and heat transfer information are exchanged between both models at each time step. Graphics processing unit computing is also applied to CityFFD for simulation speedup. The simulation of the 1971 Montreal snowstorm of the century was conducted as a case study of more than 1500 buildings of an island near Montreal, Canada for the investigation of their resilience against the three-day power outage due to the storm. Building retrofit analysis was also conducted to evaluate the added level of resilience. The results show that the proposed platform can produce high-resolution results of building thermal load, microclimate condition, and building behavior during weather extremes.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1402-1417
    DOI: 10.1016/j.apenergy.2019.04.192
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    1. Lee, Tae-Hwy & Ullah, Aman & Wang, He, 2018. "The second-order bias of quantile estimators," Economics Letters, Elsevier, vol. 173(C), pages 143-147.
    2. 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.
    3. Shangwei Zhao & Aman Ullah & Xinyu Zhang, 2018. "A Class of Model Averaging Estimators," Working Paper series 18-11, Rimini Centre for Economic Analysis.
    4. Xiangyang Cao & Bingzhong Zhou & Qiang Tang & Jiaqi Li & Donghui Shi, 2018. "Urban Wasteful Transport and Its Estimation Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    5. Yong Chen & James Lam & Jun Shen & Baozhu Du & Panshuo Li, 2018. "Reachable set estimation for switched positive systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(11), pages 2341-2352, August.
    6. Zhao, Shangwei & Ullah, Aman & Zhang, Xinyu, 2018. "A class of model averaging estimators," Economics Letters, Elsevier, vol. 162(C), pages 101-106.
    7. Shujie Ma & Oliver Linton & Jiti Gao, 2018. "Estimation in semiparametric quantile factor models," CeMMAP working papers CWP07/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Itf, 2018. "Motorway Cost Estimation Review: The Case of Slovakia," International Transport Forum Policy Papers 45, OECD Publishing.
    9. Joergen Oerstroem Moeller, 2018. "An Attempt at a Stakeholder Analysis," World Scientific Book Chapters, in: Euston Quah & Tsiat Siong Tan (ed.), Pollution Across Borders Transboundary Fire, Smoke and Haze in Southeast Asia, chapter 12, pages 137-144, World Scientific Publishing Co. Pte. Ltd..
    10. Yunzhang Zhu & Lexin Li, 2018. "Multiple matrix Gaussian graphs estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(5), pages 927-950, November.
    11. 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.
    12. 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.
    13. 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.
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    Cited by:

    1. Hong, Tianzhen & Ferrando, Martina & Luo, Xuan & Causone, Francesco, 2020. "Modeling and analysis of heat emissions from buildings to ambient air," Applied Energy, Elsevier, vol. 277(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. Azam Ghezelbash & Jay Liu & Seyed Hamed Fahimifard & Vahid Khaligh, 2024. "Exploring the Influence of the Digital Economy on Energy, Economic, and Environmental Resilience: A Multinational Study across Varied Carbon Emission Groups," Sustainability, MDPI, vol. 16(7), pages 1-22, April.
    4. Wang, Chong & Ju, Ping & Wu, Feng & Pan, Xueping & Wang, Zhaoyu, 2022. "A systematic review on power system resilience from the perspective of generation, network, and load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    5. Katal, Ali & Mortezazadeh, Mohammad & Wang, Liangzhu (Leon) & Yu, Haiyi, 2022. "Urban building energy and microclimate modeling – From 3D city generation to dynamic simulations," Energy, Elsevier, vol. 251(C).

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