Developing a common approach for classifying building stock energy models

Abstract

Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can substantially reduce emissions from the building stock are key components of broader efforts to mitigate climate change and achieve sustainable development goals. Models that represent the energy use of the building stock at scale under various scenarios of technology deployment have become essential tools for the development and assessment of such strategies. Within the past decade, the capabilities of building stock energy models have improved considerably, while model transferability and sharing has increased. Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 on Building Energy Epidemiology. Drawing from existing classification studies, we propose a multi-layer quadrant scheme that classifies modeling techniques by their design (top-down or bottom-up) and degree of transparency (black-box or white-box); hybrid techniques are also addressed. The quadrant scheme is unique from previous classification approaches in its non-hierarchical organization, coverage of and ability to incorporate emerging modeling techniques, and treatment of additional modeling dimensions. The new classification framework will be complemented by a reporting protocol and online registry of existing models as part of ongoing work in Annex 70 to increase the interpretability and utility of building stock energy models for energy policy making.

US building energy efficiency and flexibility as an electric grid resource

“…Estimates of building efficiency and flexibility potential were generated using a hybrid building stock energy modeling approach that incorporates both top-down and bottom-up elements. 63 Development of potential estimates follows four steps: (1) definition of building efficiency and flexibility measures sets, (2) determination of regional power system needs, (3) development of hourly end-use load profiles at the building-level for representative locations and building types, with and without measures applied, and (4) scaling of baseline and measure end-use load profiles across the building stock within each modeled region. The following subsections outline key information for reproducing our methods; further details about certain methodological elements are found in the supplemental experimental procedures section.…”

Material flows and GHG emissions from housing stock evolution in US counties, 2020–60

“…The need to reduce embodied emissions becomes clearer when considering the limited remaining timeframe and carbon budgets for keeping climate change within 1.5-2.0°C warming. Building stock models are widely used to estimate building and material stocks and flows (Augiseau & Barles 2017;Lanau et al 2019), energy and GHG emissions from building energy use (Langevin et al 2020), and, in limited cases, GHG emissions from both building construction and energy use (Pauliuk et al 2021;. The role of vacancies, and their influence on building construction, demolition, and material flows, is beginning to receive more attention in building stock models, particularly in regions with declining population and growing vacancy rates (Deilmann et al 2009;Wuyts et al 2020).…”

U.S. Building Energy Efficiency and Flexibility as an Electric Grid Resource

“…Estimates of building efficiency and flexibility potential were generated using a hybrid building stock energy modeling approach [56] that incorporates both top-down and bottom-up elements. Development of potential estimates followed four steps: 1) definition of building efficiency and flexibility measures and scenarios, 2) determination of regional power system needs, 3) development of sub-annual end use load profiles for representative residential and commercial building types, with and without measures applied, and 4) scaling of baseline and measure end use load profiles across the building stock within each modeled region.…”