Real-time activity recognition for energy efficiency in buildings

Real-time activity recognition for energy efficiency in buildings •An unsupervised framework to detect activities and potential savings in real-time.•Eliminating the need for collecting labeled activity data for training while achieving a high performance.•Three sub-algorithms for action detection, activity recognition and waste estimation.•Experimental validation in a testbed office with five occupants and two single-occupancy apartments.•The framework could potentially be integrated with automation module for appliance control. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Energy Elsevier

Real-time activity recognition for energy efficiency in buildings

Real-time activity recognition for energy efficiency in buildings

Applied Energy 211 (2018) 146–160 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy Real-time activity recognition for energy efficiency in buildings a a b, c Simin Ahmadi-Karvigh , Ali Ghahramani , Burcin Becerik-Gerber , Lucio Soibelman Sonny Astani Dept. of Civil and Environmental Engineering, Univ. of Southern California, KAP 217, 3620 South Vermont Ave., Los Angeles, CA 90089-2531, United States Sonny Astani Dept. of Civil and Environmental Engineering, Univ. of Southern California, KAP 224C, 3620 South Vermont Ave., Los Angeles, CA 90089-2531, United States Sonny Astani Dept. of Civil and Environmental Engineering, Univ. of Southern California, KAP 210A, 3620 South Vermont Ave., Los Angeles, CA 90089-2531, United States HIGHLIGHTS An unsupervised framework to detect activities and potential savings in real-time. Eliminating the need for collecting labeled activity data for training while achieving a high performance. Three sub-algorithms for action detection, activity recognition and waste estimation. Experimental validation in a testbed office with five occupants and two single-occupancy apartments. The framework could potentially be integrated with automation module for appliance control. ARTICLE I NFO ABSTRACT Keywords: More than half of the electricity in residential and commercial buildings is consumed by lighting systems and Building energy efficiency appliances. Consumption by these service systems is directly associated with occupant activities. By recognizing Building automation activities and identifying the associated possible energy savings, more effective strategies can be developed to Activity recognition design better buildings and automation systems. In line with this motivation, using inductive and deductive Appliance control reasoning, we introduce a framework to detect occupant activities and potential wasted energy consumption and Waste detection peak-hour usage that could...
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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0306-2619
D.O.I.
10.1016/j.apenergy.2017.11.055
Publisher site
See Article on Publisher Site

Abstract

•An unsupervised framework to detect activities and potential savings in real-time.•Eliminating the need for collecting labeled activity data for training while achieving a high performance.•Three sub-algorithms for action detection, activity recognition and waste estimation.•Experimental validation in a testbed office with five occupants and two single-occupancy apartments.•The framework could potentially be integrated with automation module for appliance control.

Journal

Applied EnergyElsevier

Published: Feb 1, 2018

References

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