A longitudinal study of thermostat behaviors based on climate, seasonal, and energy price considerations using connected thermostat data
Building and Environment , Volume 139 p. 199- 210
While previous studies have attempted to understand and predict users' behaviors and preferences for residential thermostats, they have been restricted by a lack of available data. Because of practical constraints, researchers previously relied on short observation periods, small sample groups, and/or participants close in physical location. The advent of the connected thermostat and its inherent centralized data collection now allows for such studies to be performed without the onus of data collection. Specifically, in this article we focus on the ‘Donate Your Data’ dataset made available by the thermostat manufacturer ecobee Inc. The dataset, consisting of more than 10,000 connected thermostats installed across North America and spanning multiple years, was used to investigate how users' comfort decisions are affected by exterior stimuli such as climate regions, seasonal patterns, and utility rates. Our analysis indicates that seasonality and climate region affected user preferences while utility rates did not contribute to meaningful variation in behavior. Further investigation explored if behavioral user types could be identified based on variation in occupied and unoccupied setpoints, thermostat overrides with holds, or heating and cooling setpoint selection. We did not find distinct user clusters to be identifiable based on any of the metrics; rather, occupant behavior in the population appeared to span more of a continuum across each metric.
|Connected thermostat, Residential, User behavior, User preferences|
|Building and Environment|
|Organisation||Department of Civil and Environmental Engineering|
Huchuk, B. (Brent), O'Brien, W, & Sanner, S. (Scott). (2018). A longitudinal study of thermostat behaviors based on climate, seasonal, and energy price considerations using connected thermostat data. Building and Environment, 139, 199–210. doi:10.1016/j.buildenv.2018.05.003