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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | It is too Hot: An In-Situ Study of Three Designs for Heating |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | Conference on Human Factors in Computing Systems (CHI) |
| Event Type | conference |
| Event Location | San Jose, CA |
| Event Start Date | May 7 - 2016 |
| Event End Date | May 12 - 2016 |
| Place of Publication | New York, USA |
| Publisher | ACM |
| Abstract Text | Smart energy systems that leverage machine learning techniques are increasingly integrated in all aspects of our lives. To better understand how to design user interaction with such systems, we implemented three different smart thermostats that automate heating based on users’ heating preferences and real-time price variations. We evaluated our designs through a field study, where 30 UK households used our thermostats to heat their homes over a month. Our findings through thematic analysis show that the participants formed different understandings and expectations of our smart thermostat, and used it in various ways to effectively respond to real-time prices while maintaining their thermal comfort. Based on the findings, we present a number of design and research implications, specifically for designing future smart thermostats that will assist us in controlling home heating with real-time pricing, and for future intelligent autonomous systems. |
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