  Demand-side load reduction is a key benefit of Smart Grids. However, existing
demand response optimization (DR) programs fail to effectively leverage the
near-realtime information available from smart meters and Building Area
Networks to respond dynamically to changing energy use profiles. We investigate
the use of semantic Complex Event Processing (CEP) patterns to model and detect
dynamic situations in a campus microgrid to facilitate adaptive DR. Our focus
is on demand-side management rather than supply-side constraints. Continuous
data from information sources like smart meters and building sensors are
abstracted as event streams. Event patterns for situations that assist with DR
are detected from them. Specifically, we offer a taxonomy of event patterns
that can guide operators to define situations of interest and we illustrate its
efficacy for DR by applying these patterns on realtime events in the USC Campus
microgrid using our CEP framework
