A methodology is presented that uses readily available information such as energy consumption data, limited building characteristics, and local daily temperature data to identify energy-inefficient homes in a heating-dominated climate. Specifically, this methodology is applied to 327 owner-occupied, single-family homes in Boulder, Colorado, which are compared to simulated prototype homes. A home’s energy-efficiency is characterized by its construction properties, such as insulation R-values, infiltration rates, and mechanical equipment efficiencies. Previous research indicates a close relationship between these properties and inverse modeling parameters, such as the heating slope (HS) values from variable-base degree-day (VBDD) models. The methodology compares the HS values from VBDD models of monthly natural gas consumption data to simulated HS values of reference homes. The difference, ΔHS, is the primary criterion for quantifying a home’s energy-efficiency and energy retrofit potential. To validate the results of the methodology, the results from a detailed energy assessment of a field-test home are used. Using the natural gas consumption noted in the utility data and historical weather data for the dates of bill, a VBDD model is created and the HSfield-test is calculated. HSreference of a 2009-IECC reference home of identical size is calculated and the difference, ΔHS, is calculated. Using UA-values and mechanical efficiencies from the energy assessment report, the theoretical HS values are calculated for both the assessed home and the reference home. The difference, ΔHStheoretical, is calculated. Overall, a 24% difference is found between the ΔHS and ΔHStheoretical. While the accuracy can be improved, the implication is that the energy-efficiency of homes can be inferred from inverse modeling of utility data under a specific set of conditions.

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