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Urban building energy and microclimate modeling – From 3D city generation to dynamic simulations

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

Abstract

Dynamic urban simulations often face three main challenges: 3D digital city generations, building archetype creations, and inclusions of urban microclimate impacts due to limited data and computing resources available. This study introduces a new approach for the 3D city generation by integrating publicly available data sets (OpenStreetMap and Microsoft footprints) and a free program (Google Earth). These data sets provide 2D building footprints, whereas Google Earth provides digital surface models of terrains and buildings. The building archetype library of non-geometrical properties was created based on building types and years of constructions in the form of shapefiles joined with the 3D city data through QGIS. The proposed workflow also includes the dynamic integration of urban microclimate (CityFFD) and building thermal/energy models (CityBEM). The dynamic simulations were achieved using weather station data as boundary conditions, including air temperature, solar radiation, and wind speed and direction, instead of typical meteorological year data. The transient microclimate results were validated using local weather station data, and dynamic energy simulation results were validated using measured power consumption data. The study provides a solution to dynamic urban building energy and microclimate modeling by publicly available data sets and tools.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007204
    DOI: 10.1016/j.energy.2022.123817
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    References listed on IDEAS

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    1. 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.
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    Citations

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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. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    3. Anca Patricia Grădinaru & Ana-Cornelia Badea & Petre Iuliu Dragomir & Gheorghe Badea, 2023. "Integrating Cadastral Data with Seismic Risk Data in an Online Building Database for the Historical Centre of Bucharest City," Land, MDPI, vol. 12(8), pages 1-30, August.

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