Building America Team: University of Oklahoma
Partners: University of Miami and Ecobee
Smart thermostat data on home thermal loads, weather impacts, envelope properties, and internal gains are being used to develop and validate an integrated system to provide automated fault detection and optimized controls.
Smart thermostats collect data that could be employed to advance HVAC controls and energy management in homes. However, there is currently no framework that can utilize data on comfort, occupancy, weather, energy use, time of use electricity pricing, and home design to provide fault detection and optimized control. In this project, the University of Oklahoma will develop and validate an integrated system that uses a home’s thermal loads, weather impacts, envelope properties, and internal gains to detect system degradation and optimize controls with no effort by the homeowner.
The computationally efficient, self-learning validated home thermal model uses characteristics based on codes requirements and in situ measured efficiencies of three interacting thermal components: heating and cooling equipment, air distribution system, and home envelope. The developed system will be validated by implementing the algorithms in smart thermostats in at least 10 homes. Technical findings will be shared with smart thermostat manufacturers to encourage them to implement the technology as part of their product lines.