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.
Coworkers: Erika Mata
Report number: A2500
Authors: Erika Mata, J. Langevin, J.L. Reyna, S. Ebrahimigharehbaghi, N. Sandberg, P. Fennell, C. Nägeli, J. Laverge, M. Delghust, M. Van Hove, J. Webster, F. Federico, M. Jakob, C. Camarasa
Published in: Renewable and Sustainable Energy Reviews (2020) 133, 110276