Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine
Joint Authors
Luo, Suhuai
Jiang, Lei
Platt, Glenn
Li, Jiaming
West, Sam
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-11-19
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current.
This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning.
In power load event detection, the paper presents a new transient detection algorithm.
By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off.
The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed.
The load classification method is composed of two processes including frequency feature analysis and support vector machine.
The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods.
The load classification method has achieved more than ninety percent recognition rate.
American Psychological Association (APA)
Jiang, Lei& Li, Jiaming& Luo, Suhuai& West, Sam& Platt, Glenn. 2012. Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine. Applied Computational Intelligence and Soft Computing،Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-495147
Modern Language Association (MLA)
Jiang, Lei…[et al.]. Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine. Applied Computational Intelligence and Soft Computing No. 2012 (2012), pp.1-10.
https://search.emarefa.net/detail/BIM-495147
American Medical Association (AMA)
Jiang, Lei& Li, Jiaming& Luo, Suhuai& West, Sam& Platt, Glenn. Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine. Applied Computational Intelligence and Soft Computing. 2012. Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-495147
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-495147