Residential retrofits at scale: opportunity identification, saving estimation, and personalized messaging based on communicating thermostat data

Abstract

Basic insulation and heating system retrofits of existing homes could achieve annual energy savings of up to $4–5 billion in the USA. However, current US utility energy efficiency (EE) programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and on-line advertising that lack specificity about particular home retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments that can be inconvenient to homeowners, expensive, and of variable accuracy. Our paper discusses using communicating thermostat data to significantly increase the customer uptake of energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. We extended our previously developed gray-box building model to identify physical building parameters corresponding to the target retrofit opportunities, i.e., whole-home R value, air leakage characteristics, and heating system efficiency. The estimated R values and air leakage characteristics compare favorably with the ground truth. The validated algorithms can calculate home-specific energy savings estimate for each ECM, and the algorithm outputs can be used by utility EE programs to formulate home-specific retrofit offers.