A modular framework for quantifying the impact of multi-domain modelling assumptions on urban building energy demand
Bibliographic Data
| ID | 24218256 |
|---|---|
| Authors | Mais Karaki (0009-0000-6995-648X), Elie Azar (0000-0001-7273-4853) |
| Year | 2026 |
| Volume | 151 |
| Pages | 107952 |
| Publication date | 2026-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2026.107952 |
| OpenAlex | W7214526078 |
| Language | EN |
| References cited | 40 |
Urban Building Energy Modelling (UBEM) is an established approach for evaluating urban energy demand and informing infrastructure planning and policy. However, UBEM workflows often rely on simplified modelling assumptions and input representations that overlook evolving urban and climate conditions. This study presents a modular multi-domain framework that quantifies the impact of three urban factors on energy demand: urban context, climate variability (including site-specific monitored weather and future weather projections), and electric vehicle (EV) adoption. The framework is demonstrated through a case study of a university campus in Ottawa, ON, Canada. An energy model of the campus was developed using the City Energy Analyst (CEA) platform and was calibrated against local energy benchmarks. An extensive parametric analysis showed that modelling domain representations could significantly influence urban energy predictions. While urban context exhibited a minor effect on energy demand (0.5%), climate scenarios could reduce heating demand by up to 34.9% and increase cooling electricity demand by 13.8%, while EV charging could increase total electricity use by an additional 7.4%. When combined, the domains increased electricity demand by up to 21.2%, but the increase was largely offset by a reduction in natural gas demand due to warmer climate conditions. As a result, total campus energy use remained within a narrow range of -7.9% to +4.0% relative to the baseline, masking substantial shifts among end uses. The paper then outlines implications for research, practice, and policymaking, while highlighting the transferability of the proposed modelling framework to other cities and contexts.
climate variability · electric vehicle charging · energy demand uncertainty · heating and cooling demand · model assumptions · natural gas demand · parametric study · university campus energy · urban building energy modeling · urban context · Building Energy and Comfort Optimization · Integrated Energy Systems Optimization · Urban Heat Island Mitigation
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| Highly cited | No |
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