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 21 University of Waterloo, Waterloo, ON, Canada2 Institute for Integrated Energy Systems, University of Victoria, Victoria, BC, CanadaAbstract: 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 drivenPaper:esim2022_272