  Though distribution system operators have been adding more sensors to their
networks, they still often lack an accurate real-time picture of the behavior
of distributed energy resources such as demand responsive electric loads and
residential solar generation. Such information could improve system
reliability, economic efficiency, and environmental impact. Rather than
installing additional, costly sensing and communication infrastructure to
obtain additional real-time information, it may be possible to use existing
sensing capabilities and leverage knowledge about the system to reduce the need
for new infrastructure. In this paper, we disaggregate a distribution feeder's
demand measurements into: 1) the demand of a population of air conditioners,
and 2) the demand of the remaining loads connected to the feeder. We use an
online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time
distribution feeder measurements as well as models generated from historical
building- and device-level data. We develop two implementations of the
algorithm and conduct case studies using real demand data from households and
commercial buildings to investigate the effectiveness of the algorithm. The
case studies demonstrate that DFS can effectively perform online disaggregation
and the choice and construction of models included in the algorithm affects its
accuracy, which is comparable to that of a set of Kalman filters.
