  The large scale deployment of Advanced Metering Infrastructure among
residential energy customers has served as a boon for energy systems research
relying on granular consumption data. Residential Demand Response aims to
utilize the flexibility of consumers to reduce their energy usage during times
when the grid is strained. Suitable incentive mechanisms to encourage customers
to deviate from their usual behavior have to be implemented to correctly
control the bids into the wholesale electricity market as a Demand Response
provider. In this paper, we present a framework for short term load forecasting
on an individual user level, and relate nonexperimental estimates of Demand
Response efficacy, i.e. the estimated reduction of consumption during Demand
Response events, to the variability of user consumption. We apply our framework
on a data set from a residential Demand Response program in the Western United
States. Our results suggest that users with more variable consumption patterns
are more likely to reduce their consumption compared to users with a more
regular consumption behavior.
