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


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 use within an actual house. However, 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 impactful 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 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 evaluate the potential of automated thermostat control to reduce the aggregate peak demand. A centralized model predictive controller reduces the aggregate peak load by adjusting the thermostat set points to pre-cool houses and staggers the time A/C units turn on.


Krystian Perez is a PhD student in chemical engineering at the University of Texas at Austin working under Drs. Edgar and Baldea. He earned his B.S. degree in chemical engineering from Brigham Young University in Utah. He is interested in developing residential neighborhood models based on the human activity patterns, weather trends and first principles of an individual home. From this model he would like to determine the most efficient means to control electric loads, use alternative energy sources (e.g. photovoltaics) and energy storage devices (e.g. thermal storage tanks) to mitigate peak energy demand at the level of an entire residential community. He has worked with smart meter data from Pecan Street Research Institute.


National Instruments, 11500 N Mopac Expy, Austin, TX 78759 – Building C