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

Hsueh Hsien Chang; Hong Tzer Yang; Ching Lung Lin

doi:10.1007/978-3-540-92719-8_60

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

Hsueh Hsien Chang, Hong Tzer Yang, Ching Lung Lin

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Computer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers
Pages	664-674
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-540-92719-8_60
Publication status	Published - 2008 Dec 1
Event	11th International Conference on Computer Supported Cooperative Work in Design IV, CSCWD 2007 - Melbourne, VIC, Australia Duration: 2007 Apr 26 → 2007 Apr 28

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5236 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Conference on Computer Supported Cooperative Work in Design IV, CSCWD 2007
Country	Australia
City	Melbourne, VIC
Period	07-04-26 → 07-04-28

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-92719-8_60

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Chang, H. H., Yang, H. T., & Lin, C. L. (2008). Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. In Computer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers (pp. 664-674). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5236 LNCS). https://doi.org/10.1007/978-3-540-92719-8_60

Chang, Hsueh Hsien ; Yang, Hong Tzer ; Lin, Ching Lung. / Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. Computer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers. 2008. pp. 664-674 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Chang, HH, Yang, HT & Lin, CL 2008, Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. in Computer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5236 LNCS, pp. 664-674, 11th International Conference on Computer Supported Cooperative Work in Design IV, CSCWD 2007, Melbourne, VIC, Australia, 07-04-26. https://doi.org/10.1007/978-3-540-92719-8_60

Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. / Chang, Hsueh Hsien; Yang, Hong Tzer; Lin, Ching Lung.

Computer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers. 2008. p. 664-674 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5236 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

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Chang HH, Yang HT, Lin CL. Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. In Computer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers. 2008. p. 664-674. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-92719-8_60