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
University of Orléans, France
;
2
University of Bordj Bou Arréridj, Algeria
;
3
University of Tours, France
Keyword(s):
Electrical Appliances Identification, Energy Disaggregation, Harmonic Analysis, Hidden Markov Models (HMM), Non-Intrusive Load Monitoring (NILM), Parameter Relevance, Short-Time Fourier Series (STFS), Smart Grids, Transient and Steady-state Electrical Signal Analysis.
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:
The electrical appliances identification problem is gaining a rapidly growing interest these past few years due to the recent need of this information in the new smart grid configuration. In this work, we propose to construct an appliance identification system based on the use of Hidden Markov Models (HMM) to model transient and steady-state electrical current signals. For this purpose, we investigate the usefulness of different choices for the proposed identification system such as: the use of the transient and the steady-state current signals, the use of even and odd-order harmonics as features, and the optimal number of features to take into account. This work also discusses the choice of the Short-Time Fourier Series (STFS) coefficients as adapted features for the representation of transient and steady-state current signals.