  The widespread deployment of Advanced Metering Infrastructure has made
granular data of residential electricity consumption available on a large
scale. Smart meters enable a two way communication between residential
customers and utilities. One field of research that relies on such granular
consumption data is Residential Demand Response, where individual users are
incentivized to temporarily reduce their consumption during periods of high
marginal cost of electricity. To quantify the economic potential of Residential
Demand Response, it is important to estimate the reductions during Demand
Response hours, taking into account the heterogeneity of electricity users. In
this paper, we incorporate latent variables representing behavioral archetypes
of electricity users into the process of short term load forecasting with
Machine Learning methods, thereby differentiating between varying levels of
energy consumption. The latent variables are constructed by fitting Conditional
Mixture Models of Linear Regressions and Hidden Markov Models on smart meter
readings of a Residential Demand Response program in the western United States.
We observe a notable increase in the accuracy of short term load forecasts
compared to the case without latent variables. We then estimate the reductions
during Demand Response events conditional on the latent variables, and discover
a higher DR reduction among users with automated smart home devices compared to
those without.
