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

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.