Scheduled System Maintenance on December 17th, 2014:
IEEE Xplore will be upgraded between 2:00 and 5:00 PM EST (18:00 - 21:00) UTC. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Feature Extraction of Non-intrusive Load-Monitoring System Using Genetic Algorithm in Smart Meters

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

4 Author(s)
Hsueh-Hsien Chang ; Dept. of Electron. Eng., Jin-Wen Univ. of Sci. & Technol., Taipei, Taiwan ; Po-Ching Chien ; Lung-Shu Lin ; Nanming Chen

This paper proposes non-intrusive load-monitoring (NILM) techniques using artificial neural networks (ANN) in combination with genetic algorithm (GA) to identify load demands and improve recognition accuracy of non-intrusive load-monitoring results. The feature extraction method of genetic algorithm can improve the efficiency of load identification and computational time under multiple operations. After comparing various training algorithms and classifiers in terms of artificial neural networks due to various factors that determine whether a network is being used for pattern recognition, the back propagation artificial neural network (BP-ANN) classifier is adopted in the load identification process. Additionally, in combination with electromagnetic transients program (EMTP) simulations and measurements on site, extracting the features of power signatures can lead to accurate load identifications and is a significant feature in smart meters.

Published in:

e-Business Engineering (ICEBE), 2011 IEEE 8th International Conference on

Date of Conference:

19-21 Oct. 2011