research-article

CARL: activity-aware automation for energy efficiency

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UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

Pages 939 - 946

https://doi.org/10.1145/2638728.2641554

Published: 13 September 2014 Publication History

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Abstract

Society is becoming increasingly aware of the impact that our lifestyle choices have on energy usage and the environment. This paper explores the hypothesis that ubiquitous computing technologies can be used to understand this impact and to provide activity-aware interventions to reduce energy consumption. Specifically, we introduce a method to provide energy-efficient home automation based on the recognition of activities and their associated devices. We describe CARL (CASAS Activity-based Resource Limitation), a prototype energy-efficient smart home, and evaluate the performance of our activity-aware automation when using both historic and real-time sensor data to drive intelligent home automation.

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Cited By

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Vučković APitić G(2022)New technologies in energy management systems of buildingsEkonomika preduzeca10.5937/EKOPRE2202075P70:1-2(75-86)Online publication date: 2022
https://doi.org/10.5937/EKOPRE2202075P
Chuang YChen LLiu YMandryk RHancock MPerry MCox A(2018)Design Vocabulary for Human--IoT Systems CommunicationProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3173848(1-11)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3173848
Ahmadi-Karvigh SGhahramani ABecerik-Gerber BSoibelman L(2018)Real-time activity recognition for energy efficiency in buildingsApplied Energy10.1016/j.apenergy.2017.11.055211(146-160)Online publication date: Feb-2018
https://doi.org/10.1016/j.apenergy.2017.11.055
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Index Terms

CARL: activity-aware automation for energy efficiency
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

September 2014

1409 pages

ISBN:9781450330473

DOI:10.1145/2638728

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2014

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Funding Sources

National Institutes of Health
Division of Computer and Network Systems
Life Sciences Discovery Fund

Conference

UbiComp '14

Sponsor:

UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

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Vučković APitić G(2022)New technologies in energy management systems of buildingsEkonomika preduzeca10.5937/EKOPRE2202075P70:1-2(75-86)Online publication date: 2022
https://doi.org/10.5937/EKOPRE2202075P
Chuang YChen LLiu YMandryk RHancock MPerry MCox A(2018)Design Vocabulary for Human--IoT Systems CommunicationProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3173848(1-11)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3173848
Ahmadi-Karvigh SGhahramani ABecerik-Gerber BSoibelman L(2018)Real-time activity recognition for energy efficiency in buildingsApplied Energy10.1016/j.apenergy.2017.11.055211(146-160)Online publication date: Feb-2018
https://doi.org/10.1016/j.apenergy.2017.11.055
Salem HSayed-Mouchaweh MBen Hassine A(2016)A Review on Machine Learning and Data Mining Techniques for Residential Energy Smart Management2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2016.0195(1073-1076)Online publication date: Dec-2016
https://doi.org/10.1109/ICMLA.2016.0195

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Recommendations

Annotating smart environment sensor data for activity learning

On the Black-Box Stand-by Recognition Strategies in Smart Homes Environments

Hacking the natural habitat: an in-the-wild study of smart homes, their development, and the people who live in them