Meters to Models: Using Smart Meter Data to Predict and Control Home Energy Use

Access to smart meter data in the United States presents an opportunity to better understand residential energy consumption and energy-related behaviors. Air-conditioning (A/C) use, in particular, is a highly variable and significant contributor to residential energy demand. Most current building simulation software tools require intricate detail and training to accurately model A/C electricity consumption within an actual house. While there has been substantial research into modeling building energy, little has been done using energy data from actual residential houses. Integrating existing modeling software and empirical data has the potential to create highly portable and accurate models. Reduced-order models (ROM) are low-dimensional approximations of more complex models that use only the most important variables. In this paper, we report on the development of ROMs for 41 physical houses in Austin, Texas, using smart meter data. These models require outdoor dry bulb temperature, thermostat set points and A/C energy use data as inputs to regress model coefficients. A non-intrusive load monitoring technique is used to disaggregate A/C electricity consumption from whole-house electricity data reported by smart meters. Thermostat set points are provided by smart thermostats. Once trained, the models can use thermostat set points and dry bulb temperatures to predict A/C loads. The ROMs are used to simulate the potential of this group of houses to reduce peak demand using automated thermostat control schemes.