A HIGH-GRANULARITY APPROACH TO MODELING ENERGY CONSUMPTION AND SAVINGS POTENTIAL IN THE U.S. RESIDENTIAL BUILDING STOCK
Building simulations are increasingly used in various
applications related to energy efficient buildings. For
individual buildings, applications include: design of
new buildings, prediction of retrofit savings, ratings,
performance path code compliance and qualification for
incentives. Beyond individual building applications,
larger scale applications (across the stock of buildings
at various scales: national, regional and state) include:
codes and standards development, utility program
design, regional/state planning, and technology
assessments. For these sorts of applications,
representative buildings are needed for simulations to
predict performance of the entire population of
Focusing on the U.S. single-family residential building
stock, this paper will describe how multiple data
sources for building characteristics are combined into a
highly-granular database that preserves the important
interdependencies of the characteristics. We will
present the sampling technique used to generate a
representative set of thousands (up to hundreds of
thousands) of building models. We will also present
results of validation against building stock consumption
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