ThermoCoach: A Study of Occupancy-Based Schedule Recommendations on Energy Cost and User Comfort

Pisharoty, Devika, Computer Science - School of Engineering and Applied Science, University of Virginia
Whitehouse, Cameron, Department of Computer Science, University of Virginia

The largest portion of a home's energy consumption is attributed to its Heating, Ventilation and Cooling system(HVAC). Since the early 1900s, programmable thermostats have been studied as a potential tool to achieve energy savings in the home. However, studies have shown that conventional programmable
thermostats are not used to their full potential due to several factors- difficult to use interfaces, lack of knowledge of working of HVACs and fading user interaction with the thermostats over time. To overcome this, `Smart' thermostats detect the occupancy trends of a home and auto-generate schedules; thus eliminating the need for users to program their thermostats. Studies indicate that feedback of energy consumption has the potential to keep homeowners engaged with the energy usage in their homes and motivates them
to take action to reduce energy consumption. This thesis presents ThermoCoach- An occupancy-based self-programming thermostat with eco-feedback. ThermoCoach uses occupancy sensors to detect occupancy
patterns of a home and generates customized recommendations of thermostat schedules for a home. Schedule recommendations are provided to users through an online interface. ThermoCoach is evaluated against conventional programmable thermostats and the Nest Learning thermostat. For this pilot study, sensing systems were installed in thirty nine homes for a period of three months. ThermoCoach schedules reduced energy cost by 5% while Nest schedules increased costs by 7% when compared to programmable thermostats.

MS (Master of Science)
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