Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads

Hsueh Hsien Chang, Hong Tzer Yang, Ching Lung Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

56 Citations (Scopus)

Abstract

This paper proposes the use of neural network classifiers to evaluate back propagation (BP) and learning vector quantization (LVQ) for feature selection of load identification in a non-intrusive load monitoring (NILM) system. To test the performance of the proposed approach, data sets for electrical loads were analyzed and established using a computer supported program - Electromagnetic Transient Program (EMTP) and onsite load measurement. Load identification techniques were applied in neural networks. The efficiency of load identification and computational requirements was analyzed and compared using BP or LVQ classifiers method. This paper revealed some contributions below. The turn-on transient energy signatures can improve the efficiency of load identification and computational time under multiple operations. The turn-on transient energy has repeatability when used as a power signature to recognize industrial loads in a NILM system. Moreover, the BP classifier is better than the LVQ classifier in the efficiency of load identification and computational requirements.

Original languageEnglish
Title of host publicationComputer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers
Pages664-674
Number of pages11
DOIs
Publication statusPublished - 2008 Dec 1
Event11th International Conference on Computer Supported Cooperative Work in Design IV, CSCWD 2007 - Melbourne, VIC, Australia
Duration: 2007 Apr 262007 Apr 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5236 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Computer Supported Cooperative Work in Design IV, CSCWD 2007
CountryAustralia
CityMelbourne, VIC
Period07-04-2607-04-28

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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