Differentiating between energy-efficient and inefficient single-family homes on a community scale helps identify and prioritize candidates for energy-efficiency upgrades. Prescreening diagnostic procedures can further retrofit efforts by providing efficiency information before a site-visit is conducted. We applied the prescreening diagnostic is applied to a simulated community of homes in Boulder, Colorado and analyzed energy consumption data to identify energy-inefficient homes. A home is defined as efficient if it is compliant with the prescriptive measures of the 2009 International Energy Conservation Code (IECC-2009) for Boulder, Colorado. Previous research indicates a correlation between building operational efficiency and the Heating Slope (HS) regression parameter resulting from the variable-base degree day method. We compared the HS values across a community of houses and those of an IECC-2009-compliant home to identify energy-inefficient homes on a community-scale. To simulate community-wide HS identification, we used DOE-2 energy simulation software for defined home archetypes and corresponding occupant behavior to artificially generate 567 sets of monthly natural gas consumption data Home archetypes were either compliant or incompliant at three conditioned areas; occupant effects were also simulated. Each simulation produced twelve months of natural gas use data. We used monthly energy consumption datasets to estimate the HS values with regression analysis and sorted the homes based on HS values.

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