A Bayesian perspective on Residential Demand Response using smart meter data

The widespread deployment of Advanced Metering Infrastructure has made granular data of residential electricity consumption available on a large scale. 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 data 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 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.