Learning from a learning thermostat

Abstract

Everyday systems and devices in the home are becoming smarter. In order to better understand the challenges of deploying an intelligent system in the home, we studied the experience of living with an advanced thermostat, the Nest. The Nest utilizes machine learning, sensing, and networking technology, as well as eco-feedback features. We conducted interviews with 23 participants, ten of whom also participated in a three-week diary study. Our findings show that while the Nest was well-received overall, the intelligent features of the Nest were not perceived to be as useful or intuitive as expected, in particular due to the system's inability to understand the intent behind sensed behavior and users' difficulty in understanding how the Nest works. A number of participants developed workarounds for the shortcomings they encountered. Based on our observations, we propose three avenues for future development of interactive intelligent technologies for the home: exception flagging, incidental intelligibility, and constrained engagement.