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HVAC characterisation of existing Canadian buildings for decarbonisation retrofit identification Cover

HVAC characterisation of existing Canadian buildings for decarbonisation retrofit identification

Open Access
|Oct 2025

Abstract

Building archetypes are useful in building energy simulations as they simplify the modelling process. These building archetypes are classified in the Building Technology Assessment Platform (BTAP), a database built on Natural Resources Canada building codes. There are two groups: buildings established 1980 to 2004 and buildings established before 1980. The major drawback with the BTAP archetypes is that there are no considerations made regarding changes in mechanical systems in pre-1980 buildings, nor are the impacts of this evolution examined. This study expands the available archetypes by investigating typical heating, ventilation and air conditioning (HVAC) systems used for offices and multi-unit residential buildings in the City of Toronto by analysing data from municipal and industry partner sources to determine system characteristics for each building type for each period and suggest retrofits for the selected characteristics. This study identifies common building clusters based on building topology, size and vintage to develop more varied archetypes. By increasing the granularity of existing archetypes and presenting them for ASHRAE climate zone 5 A, retrofit modelling for Canadian buildings will improve in accuracy. Both baseline and retrofit conditions are modelled in both current and decarbonised thermal and electricity source conditions to understand the relative benefit of individual building vs district utility retrofits.

Practice relevance

This study furthers the applications of archetype development in North America by developing a set of granular HVAC system characterisations to better model existing buildings. This will support urban- and portfolio-scale energy modelling by enabling rapid simulation of existing buildings with increased accuracy versus existing ‘reference model’ methods.

DOI: https://doi.org/10.5334/bc.537 | Journal eISSN: 2632-6655
Language: English
Submitted on: Jan 24, 2025
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Accepted on: Aug 27, 2025
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Published on: Oct 3, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Jackson Adebisi, J. J. McArthur, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.