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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. (More)

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Paper citation in several formats:
Nait Meziane, M.; Hacine-Gharbi, A.; Ravier, P.; Lamarque, G.; Le Bunetel, J. and Raingeaud, Y. (2017). Electrical Appliances Identification and Clustering using Novel Turn-on Transient Features. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-222-6; ISSN 2184-4313, SciTePress, pages 647-654. DOI: 10.5220/0006245706470654

@conference{icpram17,
author={Mohamed {Nait Meziane}. and Abdenour Hacine{-}Gharbi. and Philippe Ravier. and Guy Lamarque. and Jean{-}Charles {Le Bunetel}. and Yves Raingeaud.},
title={Electrical Appliances Identification and Clustering using Novel Turn-on Transient Features},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2017},
pages={647-654},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006245706470654},
isbn={978-989-758-222-6},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Electrical Appliances Identification and Clustering using Novel Turn-on Transient Features
SN - 978-989-758-222-6
IS - 2184-4313
AU - Nait Meziane, M.
AU - Hacine-Gharbi, A.
AU - Ravier, P.
AU - Lamarque, G.
AU - Le Bunetel, J.
AU - Raingeaud, Y.
PY - 2017
SP - 647
EP - 654
DO - 10.5220/0006245706470654
PB - SciTePress