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Understanding context governing energy consumption in homes

Published: 26 April 2014 Publication History

Abstract

The key to designing better home energy management systems is in-depth understanding of the context underlying energy usage. The common method of inferring the underlying context is data collection through extensive sensor deployments and then deriving contextual ties between factors like occupancy and energy consumption. There is, therefore, a lack of studies that use first principle approaches like interviewing households to understand the major factors that influence energy consumption. In this work-in-progress paper, we present preliminary results from an interview-based study on households in low-income neighborhoods in Baltimore City. We show that there are several factors like house insulation, use of old appliances, and specific activities that influence energy consumption. Moreover, we have found that households in these neighborhoods are willing to volunteer their homes as testbeds for collecting contextual data and are primarily incentivized by reduction in their electricity bill.

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References

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Cited By

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  • (2016)Supporting interactive visual analytics of energy behavior in buildings through affine visualizationsProceedings of the 28th Australian Conference on Computer-Human Interaction10.1145/3010915.3010950(238-247)Online publication date: 29-Nov-2016

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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
    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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    Author Tags

    1. consumption
    2. context
    3. design
    4. energy
    5. hci

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    CHI '14
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    CHI '14: CHI Conference on Human Factors in Computing Systems
    April 26 - May 1, 2014
    Ontario, Toronto, Canada

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    CHI EA '14 Paper Acceptance Rate 1,000 of 3,200 submissions, 31%;
    Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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    • (2016)Supporting interactive visual analytics of energy behavior in buildings through affine visualizationsProceedings of the 28th Australian Conference on Computer-Human Interaction10.1145/3010915.3010950(238-247)Online publication date: 29-Nov-2016

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