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February 20, 2026Smart Cities0 citationsOpen Access

Estimating Building Air Change Rates with Multizone Models at Urban Scale: Comparative Case Studies

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YUYasemin UstaWDWilliam Stuart DolsCBCristina Bertani

Key Points

  • This study aims to improve the estimation of building-specific air change rates for urban energy modeling by using lumped-parameter airflow models.
  • Applied lumped-parameter airflow models to simulate interzone airflow.
  • Solved nonlinear equations for air change rate calculations.
  • Linked wind-related boundary conditions with building models using urban geometry.
  • Compared two cities, New York City and Turin, for variability in air change rates.
  • Verified the model against CONTAM for accuracy.
  • Achieved a mean absolute percentage error of 1.2% across 120 weather scenarios.
  • Improved energy consumption predictions with building-specific air change rates, reducing average error by 27% over the heating season.

Abstract

Accurate estimation of building-specific air change rates is important for reliable urban-scale energy modeling, particularly in densely populated regions where airflow calculations must account for complex boundary conditions associated with urban geometry. This study applied lumped-parameter airflow models to simulate interzone airflow by calculating the internal pressures using simplified building representations. Air change rates were calculated by solving a system of nonlinear equations, with boundary conditions defined by localized wind inputs corrected using aerodynamic parameters extracted from three-dimensional urban geometry. By linking these wind-related boundary conditions with lumped-parameter airflow models, the methodology describes spatial variability in natural infiltration across a broad range of urban densities. Two cities were compared to test the variability in building air change rates using local boundary conditions: New York City, a dense modern city, and Turin, a typical medium-density European city. Moreover, verifying the lumped-parameter model against CONTAM (Version 3.4.0.6) showed accurate results, with a mean absolute percentage error of 1.2% across 120 simulated weather scenarios. Furthermore, comparing energy consumption predictions using building-specific air change rates to those using fixed air change rates showed improved accuracy, resulting in an average error reduction of 27% over the entire heating season for a sample building. This scalable, automated approach enables more accurate assessments of ventilation-driven energy use in compact urban areas.

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Cite This Study

Usta et al. (2026) studied this question.

synapsesocial.com/papers/6997fa5aad1d9b11b34537eahttps://doi.org/10.3390/smartcities9020037
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