One of the most demanding segments of Building decarbonization is building retrofits. For this reason, the NGCI has developed an urban building retrofit tool, with all buildings simulated individually, taking into account aspects such as shading or adjacencies and considering as many details as possible. As a second step, three scenarios with different levels of ambitions have been implemented in the tool and the demands resulting from these scenarios have been calculated, as well as the initial investment costs and operational costs. With all those calculations, a detailed Life Cycle Costing (LCC) strategy has been implemented. The article describes the robust and scalable structure that has been developed in the NGCI and the application of this structure to calculate LCC of different retrofitting scenarios in Montréal. First results for the proof of concept are plotted, and differences with the consumption of real buildings are shown. In the future developments of the tool, a calibration methodology to adjust the most essential parameters for the Urban Energy Model will be implemented.

This paper introduces a method and computational platform that combines urban building energy modeling (UBEM), life cycle assessment (LCA), and solar irradiance simulations to assess building integrated photovoltaics (BIPV) at an urban scale. Demonstrated through three BIPV scenarios applied to three case studies, the method enables computing key performance indicators (KPIs) to understand design and planning opportunities that can ultimately support decision making. The tool can predict a wide range of design approaches for BIPV deployement at urban scale. With the capacity to efficiently combine energy performance simulation with systematic KPI estimation, the tool offers stakeholders a powerful resource for creating optimized BIPV neighborhoods and fostering sustainable urban development.

Building decarbonization is an urgent, cross-disciplinary challenge. However, stakeholders often need better tools to plan this transformation. Holistic urban building energy models (UBEM) enable planners and municipal government to see the bigger picture and allow for data-driven and community-engaged planning. However, UBEM adoption remains rare, especially in smaller cities, due to technical complexity, lack of data availability, and data sharing on building stock properties and energy use. This paper describes a fully automated bottom-up UBEM simulation approach that fuses available urban data sets for rapid and building-specific energy scenario simulation using a lower-order 5R1C model. Results from this analysis would allow planners to implement best practice multi-objective, multi-stakeholder decision-making methodologies that would prove invaluable to accelerate building stock decarbonization through optimal allocation of limited resources using UBEM as a valuable decision-making tool.

Dealing with an aging building stock that is reliant on natural gas for primary energy can seem like a daunting task for utilities as they start down the path to achieving national-level decarbonization goals. Converting the two largest residential energy end uses – space and water heating – to electricity using Air Source Heat Pumps (ASHP) is quickly becoming a preferred decarbonization pathway in many jurisdictions (Reyna et al.2022). This, in combination with the upcoming IRA funding for heat pumps, is driving many utilities to offer new or increased incentives for heat pumps through utility programs. However, the conventional utility method of calculating the electricity and fuel impacts of heat pumps is still reliant on using Technical Reference Manual (TRM) engineering algorithms that are often inaccurate and do not explicitly consider important building characteristics like insulation and air leakage conditions of the home. Building energy modeling is a potential solution to the problem but has historically not been utilized for existing residential stock modeling due to the lack of prototypical starting models and the subsequent large upfront effort for model creation. NREL’s ResStock tool changes all of this and provides a clear pathway for generating electrification measure impact estimates that are anchored in the building stock characteristics of a utility territory and calibrated on an aggregate scale to metered data (Wilson 2017). This paper presents a novel methodology utilizing ResStock that is more accurate than the current simplistic energy savings estimation approach used by utilities.