Building dynamic thermal profiles of energy consumption for individuals and neighborhoods

As a way to match peaks in demand to available supply in real-time on the power grid, energy utility companies employ Demand-Response (DR) strategies. With the recent deployment of advanced metering infrastructure collecting highly granular (sub-hourly) data on consumption from millions of users system operators may now understand how demand arises down to the individual level. In this paper we present an application of a dynamic model that describes residential users' thermally-sensitive consumption using hourly electricity and weather readings. We build rich profiles at individual and group levels that may be used to inform DR programs that focus on temperature-dependent consumption such as heating and cooling. We learn individual thermal profiles for a large user sample, and describe the seasonal and time-of-day distribution of thermal regimes. Finally, we build aggregate thermal profiles for geographically-homogeneous groups of different size. We argue that aggregation leads to regularity in temperature response of energy consumption, and characterize empirically the critical size of groups needed to achieve limiting regularity.