Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Automatic Standby Power Management Using Usage Profiling and Prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Seungwoo Lee ; Dept. of Comput. Sci., Yonsei Univ., Seoul, South Korea ; Gilyoung Ryu ; Yohan Chon ; Rhan Ha
more authors

Reducing the standby power used by home appliances is critical in a household energy management system. Although significant effort has been made to minimize the standby power use of appliances, manual operation is still required to eliminate standby power usage. Additionally, the current regulation strategy of standby power typically focuses on real-power consumption, and it does not consider the apparent power and power factors. We propose an automatic standby power reduction system that is based on user-context profiling. Our system profiles and analyzes the occupancy pattern, as well as the appliance usage. The system then actively manages standby power utilization by predicting the probabilities of future appliance usage. We built a prototype smart meter to monitor and control the power lines. We also developed software that implements the proposed scheme. Our experiments, conducted for three to five weeks in four households, show that power consumption in standby mode can be reduced.

Published in:

Human-Machine Systems, IEEE Transactions on  (Volume:43 ,  Issue: 6 )