  Before a building can participate in a demand response program, its facility
managers must characterize the site's ability to reduce load. Today, this is
often done through manual audit processes and prototypical control strategies.
In this paper, we propose a new approach to estimate a building's demand
response capacity using detailed data from various sensors installed in a
building. We derive a formula for a probabilistic measure that characterizes
various tradeoffs between the available demand response capacity and the
confidence level associated with that curtailment under the constraints of
building occupant comfort level (or utility). Then, we develop a data-driven
framework to associate observed or projected building energy consumption with a
particular set of rules learned from a large sensor dataset. We apply this
methodology using testbeds in two buildings in Singapore: a unique net-zero
energy building and a modern commercial office building. Our experimental
results identify key control parameters and provide insight into the available
demand response strategies at each site.
