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To switch off the coffee-maker or not: that is the question to be energy-efficient at work

Published:26 April 2014Publication History

ABSTRACT

There are some barriers to reduce energy consumption in shared spaces where many people use common electronic devices (e.g. dilution of responsibility, the trade-off between comfort and necessity, absentmindedness, or the lack of support to foster energy-efficiency). The workplace is a challenging scenario since the economic incentives are not present to increase energy awareness. To tackle some of these issues we have augmented a shared coffee-maker with eco-feedback to turn it into a green ally of the workers. Its design rationale is twofold: Firstly, to make the coffee-maker able to learn its own usage pattern. Secondly, to communicate persuasively and in real-time to users whether it is more efficient to leave the appliance on or off during certain periods of time along the workday. The goal is to explore a human-machine team towards energy efficiency and awareness, i.e. whether giving the initiative to users to decide how to operate the common appliances, but being assisted by them, is a better choice than automation or mere informative eco-feedback.

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    • Published in

      cover image ACM Conferences
      CHI EA '14: CHI '14 Extended Abstracts on Human Factors in Computing Systems
      April 2014
      2620 pages
      ISBN:9781450324748
      DOI:10.1145/2559206

      Copyright © 2014 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 26 April 2014

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      CHI EA '14 Paper Acceptance Rate1,000of3,200submissions,31%Overall Acceptance Rate6,164of23,696submissions,26%

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