Authors:
Mohamed Nait Meziane
1
;
Abdenour Hacine-Gharbi
2
;
Philippe Ravier
1
;
Guy Lamarque
1
;
Jean-Charles Le Bunetel
3
and
Yves Raingeaud
3
Affiliations:
1
Université d'Orléans, France
;
2
University of Bordj Bou Arréridj, Algeria
;
3
University of Tours, France
Keyword(s):
Electrical Appliances Identification and Clustering, Energy Disaggregation, Non-Intrusive Load Monitoring (NILM), Sequential Forward Search (SFS) Algorithm, Supervised and Unsupervised Classification, Turn-on Transient Features, Wrappers Feature Selection.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Learning and Adaptive Control
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
Abstract:
Due to the growing need for a detailed consumption information in the context of energy efficiency, different
energy disaggregation, also called Non-Intrusive Load Monitoring (NILM), methods have been proposed.
These methods may be subdivided into supervised and unsupervised approaches. Electrical appliance classification
is one of the tasks a NILM system should perform. Depending on the chosen NILM approach, the
classification task consists of either identifying the appliances or grouping them into clusters. In this paper,
we present the results of appliance identification and clustering using the Controlled On/Off Loads Library
(COOLL) dataset. We use novel features extracted from a recently proposed turn-on transient current model
for both identification and clustering. The results show that the amplitude-related features of this model are
the most suited for appliance identification (giving a classification rate (CR) of 98.57%) whereas the enveloperelated
features are th
e most adapted for appliance clustering.
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