Abstract

Urban areas account for the majority of global energy consumption and greenhouse gas (GHG) emissions, with buildings alone responsible for nearly 40% of worldwide energy use and 30% of emissions. Achieving carbon neutrality in cities requires large-scale electrification, integration of renewable energy systems, and systematic retrofitting of existing building stocks. However, current energy system modelling tools remain limited in their ability to support these transitions. They often rely on fragmented data structures, lack interoperability with urban building energy models, depend on benchmark or static demand values rather than validated hourly simulations, and provide no automated or scalable pathways for scenario exploration, comparative assessment, or retrofit prioritization at the portfolio level.
This thesis addresses these gaps through the development of an automated, open-source Energy System Modelling Framework and a scalable Retrofit Prioritization Workflow, both integrated within the TOOLS4CITIES urban simulation platform at Concordia University’s Next-Generation Cities Institute. The Energy System Modelling Framework is built on a generic, object-oriented data model that organizes generation, storage, distribution, and emission components in a unified and extensible structure. Semi-empirical performance models for key low-carbon technologies—including heat pumps, boilers, photovoltaic systems, and thermal storage—are coupled with a life cycle cost assessment module and a multi-objective non-dominated sorting genetic algorithm II (NSGA-II) optimization engine to identify cost-effective and energy-efficient system configurations.
Complementing component-level design optimization, the Retrofit Prioritization Workflow combines measured monthly utility data with archetype-based urban building energy modelling using the PRInceton Scorekeeping Method (PRISM) and Kernel Density Estimation (KDE). This enables the detection of underperforming buildings and employs multi-criteria decision-making to generate transparent, scalable retrofit rankings across large building portfolios.
The methods are demonstrated through three sets of case studies: (1) cross-climate analysis of air-source heat pump and rooftop photovoltaic systems in Montreal and Palma de Mallorca; (2) feasibility-driven, multi-objective system design optimization for representative high-rise and mid-rise buildings; and (3) portfolio-scale retrofit prioritization and scenario assessment for 201 residential buildings from the Office municipal d’habitation de Montréal. These applications illustrate the framework’s ability to evaluate renewable integration, identify optimal electrification pathways, and support data-driven decisions on retrofit investment.
Collectively, the developed framework and workflow offer a unified, automation-ready methodology for building- and portfolio-scale energy system analysis. They provide researchers, planners, and practitioners with a flexible platform for evaluating urban decarbonization strategies, enabling more consistent, transparent, and scalable assessments of future low-carbon urban energy futures.