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

Hsueh Hsien Chang, Hong Tzer Yang, Ching Lung Lin

研究成果: Conference contribution

62 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Computer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers
頁面664-674
頁數11
DOIs
出版狀態Published - 2008 十二月 1
事件11th International Conference on Computer Supported Cooperative Work in Design IV, CSCWD 2007 - Melbourne, VIC, Australia
持續時間: 2007 四月 262007 四月 28

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5236 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other11th International Conference on Computer Supported Cooperative Work in Design IV, CSCWD 2007
國家/地區Australia
城市Melbourne, VIC
期間07-04-2607-04-28

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)

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