This paper presents findings from a field deployment that explored a design approach we call eco-coaching: giving personalized suggestions for specific actions that would reduce wasted energy. We studied ThermoCoach, which performs eco-coaching for thermostat scheduling. It senses and models occupancy patterns in a home, and provides occupants alternative suggestions for configuring their thermostat. Our study shows that eco-coaching accomplished four things. First, it made it easier for users to implement an effective thermostat schedule. Second, it supported user agency in negotiating energy savings and comfort goals. Third, it facilitated learning different scheduling strategies as well as weighing different options. Finally, it challenged users' beliefs about how well they were doing. These outcomes, in turn, were successful in getting users to employ and experiment with more efficient setback strategies. Going forward, we propose ways that eco-coaching systems could better support users in customizing and assessing the systems' recommendations.
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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.i Approval Sheet
Despite the comfort and safety that homes provide to humans, they are a hazardous environment for operating embedded sensors and controllers. In this paper, we present the design and implementation of Piloteur: a lightweight platform for robust pilot studies of sensing and control systems in the home. Upon installation, it provides end-to-end monitoring of system operation, it repairs problems whenever possible, and it alerts the operator of fatal problems that cannot be repaired. It performs these tasks while minimizing impact on software development time, long-term maintenance risks, and deployment cost. Piloteur is an open-source platform offered to the community that consolidates our experience and lessons learned from deploying hundreds of sensors and controllers in homes. In this paper, we present the design and implementation of Piloteur as well as our experiences using it to deploy over 180 endpoints across 45 different homes. Despite thousands of potentially fatal node failures, Piloteur automatically repaired nodes most of the time and only 26 physical maintenance visits were required over the course of 4 months.
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