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.

Smart Thermostats: An Experimental Facility to Test Their Capabilities and Savings Potential

“…P. Rau et al presented a study of the needs of these thermostats obtained from interviews and surveys of the consumers [9], noticing that there are still unsolved problems in the hardware and technology on this type of devices and advising the researchers and designers to focus on the user's needs. Some other reviews and detailed studies of modern thermostats can be specified [10,11] which indicate that a lot of work still has to be done in order to clearly see the benefits of this smart management of temperature.…”

“…Also, the general perception from the user's answers in the surveys and interviews shows the difficulty of knowing the real savings coming from the use of smart thermostats [9,11]. These were the original motivations of this new research.…”

Using Shape-Change to Express Dynamic Affordances of Intelligent Systems

“…As intelligent systems enter everyday lives, people often encounter very basic problems in communication [8]. Users move through interactive fields often without knowing which objects or spaces to interact with because too many systems are "faceless" and not revealing themselves to be "smart" until the user produces a correct interaction cue through the medium (s) that the system anticipates [9].…”

Designing the Expressiveness of Point Lights for Bridging Human-IoT System Communications

“…When the systems run well, they can provide users smooth experiences. However, when something unexpected happens, users usually feel frustrated because they do not know how the problem occurred [2,3]. Even when everything runs smoothly, feedbacks are also essential to reassure that this is the case.…”