Understanding specific home archetypes is essential to clarifying the market potential of a residential energy retrofit program and developing targeted marketing opportunities. In addition, home archetypes are a necessary step to help support the development of unique retrofit pathways and costing models, along with informing detailed program design.

Windfall Centre’s data science team uses a machine learning algorithm to develop statistical models of like dwellings (clusters). Once the optimal number of archetype clusters is determined, each home in the municipality is partitioned into a distinct housing archetype.

From the defined archetypes, we determine the average heating, ventilation, and air conditioning (HVAC) equipment, insulation values, air leakage, and other significant data points that fall within the developed archetypes. By looking at the retrofits performed and the energy and emission reductions from post-retrofit evaluations, we determine the most effective retrofits for each archetype.

This work requires significant computing power and access to large data sets including, municipal tax role data, Natural Resources Canada EnerGuide data, energy utility data, and other data sources that may be available locally.

For every archetype we develop a net‑zero retrofit pathway, which is a staged retrofit plan identifying all the retrofit measures required over time to reduce a homes GHG emissions to net-zero.