Paper Conference

Proceedings of eSim 2022: 12th Conference of IBPSA-Canada


Modeling household energy retrofits through data-driven archetype formulation

Rachel Annamaria Barton1,2, Mohammad Haris Shamsi 2, Mahsa Torabi2, Yongheng Zou 2, Ralph Evins 2
1 University of Waterloo, Waterloo, ON, Canada
2 Institute for Integrated Energy Systems, University of Victoria, Victoria, BC, Canada

Abstract: Existing buildings provide significant opportunities to reduce the share of the building sector on the overall energy consumption and greenhouse gas emissions. Energy efficiency retrofits have gained a huge momentum, however, the definition of optimal retrofit for a specific building is a complex process and stakeholders face significant challenges when making informed decisions. This research formulates a process workflow to model retrofits through the use of data-driven archetype modeling. The workflow derives the archetypes using data clustering and input features of the dataset. A preliminary analysis for the city of Victoria showed that the retrofit effectiveness varies significantly across the span of the formulated archetypes. Furthermore, the variation of retrofits for a single archetype signifies the behavioral aspects of implemented retrofits, giving an indication of the presence of (p)rebound effects.
Keywords: Energy retrofits, Building Performance, Furnace upgrades, Data driven