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A tiled multi-city urban objects dataset for city-scale building energy simulation.

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  • 1Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong SAR, China.

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This study introduces a new tiled urban dataset and distributed ontology to enable efficient city-scale building energy simulations. This approach overcomes computational limitations, making urban planning and microclimate analysis more feasible.

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Area of Science:

  • Urban planning and computational science
  • Environmental engineering and sustainable cities

Background:

  • Large-scale building energy simulations are crucial for urban planning but are computationally intensive.
  • Existing models lack the precision and scalability for comprehensive city-wide analysis.

Purpose of the Study:

  • To develop a scalable dataset and methodology for city-scale building energy simulation.
  • To address computational challenges and improve the accuracy of urban energy modeling.

Main Methods:

  • Creation of a tiled multi-city urban objects dataset with a distributed data ontology.
  • Incorporation of interactive relationships among urban objects (buildings, vegetation, streets).
  • Aggregation of morphological features for UrbanTiles and patch-based distributed simulations.

Main Results:

  • The dataset includes over 8 million buildings and millions of other urban objects from 30 US cities.
  • A sample test demonstrated a linear relationship between building numbers and simulation time.
  • The tiled data structure proved efficient for building microclimate estimation.

Conclusions:

  • The developed dataset and ontology enable efficient, patch-based distributed simulations for urban energy analysis.
  • This approach significantly reduces computational resource requirements for city-scale modeling.
  • The methodology supports improved urban planning and building microclimate estimation.