DocumentCode :
3613136
Title :
Non-Intrusive Appliance Load Identification Based on Higher-Order Statistics
Author :
Diego Silva Guedes, Juan ; Diego Ferreira, Danton ; Henrique Groenner Barbosa, Bruno ; Augusto Duque, Carlos ; Santiago Cerqueira, Augusto
Author_Institution :
Univ. Fed. de Juiz de Fora (UFJF), Juiz de Fora, Brazil
Volume :
13
Issue :
10
fYear :
2015
Firstpage :
3343
Lastpage :
3349
Abstract :
This paper presents a new method based on Higher-order Statistics for non-intrusive residential electrical load identification. Basically, the proposed method extracts cumulants of second and fourth order from the electric current signal of the residential electrical loads and presents these cumulants to a previously trained artificial neural network for classification. The neural network output identifies the residential electric load class of the processed signal. This study considered eleven different classes of residential electrical loads. Results were carried out from experimental electric signals and the achieved overall performance was over to 97%.
Keywords :
domestic appliances; higher order statistics; load (electric); neural nets; power engineering computing; artificial neural network; cumulants extraction; electric current signal; higher order statistics; nonintrusive appliance load identification; residential electrical load identification; residential electrical loads; Earth Observing System; Higher order statistics; Home appliances; Monitoring; RNA; Smart grids; Support vector machines; Non-intrusive monitoring; electrical loads; smart grids;
fLanguage :
English
Journal_Title :
Latin America Transactions, IEEE (Revista IEEE America Latina)
Publisher :
ieee
ISSN :
1548-0992
Type :
jour
DOI :
10.1109/TLA.2015.7387241
Filename :
7387241
Link To Document :
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