Authors:
Fateh Ghazali
1
;
Abdenour Hacine-Gharbi
1
;
Khaled Rouabah
2
and
Philippe Ravier
3
Affiliations:
1
LMSE Laboratory, University of Bordj Bou Arreridj, Bordj Bou Arréridj, Algeria
;
2
Electronics Department, University of Mohamed Boudiaf M’sila, Algeria
;
3
PRISME Laboratory, University of Orleans, Orléans, France
Keyword(s):
Non-Intrusive Load Monitoring (NILM), Electrical Appliances Identification, Statistical Feature Extraction, Discrete Wavelets Analysis, Wavelet Cepstral Coefficient (WCC), K-Nearest Neighbors (KNN), Voting Rules Method, Independent Mode of House.
Abstract:
In Electrical Appliances Identification (EAI) system, Plug Load Appliance Identification Dataset (PLAID) is largely used to develop and benchmark new methods proposed for demand management in electricity networks, more particularly, automated control, non-intrusive load planning and monitoring. Particularly, this database contains electrical signals of 11 appliance electrical appliances, recorded in several houses. In state-of-the-art, the EAI systems have used this latest PLAID designed, in two parts (one for training and the other for testing). These parts can be organized on house-dependent mode or house-independent mode. In the first mode, the signals of each appliance class and house in the testing part have examples in the training part. In opposition, in the second mode, the houses in testing part have not any example in training part. In this paper, we propose a comparative study between the performance of house-dependent EAI system and those of house independent mode system.
In addition, in order to more validate the results of the comparison study, we propose the use of other classifiers like Gaussian Mixture Model (GMM), Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN). The obtained results, based on the use of PLAID, have demonstrated that the performances of this system, in independent mode, are relatively low compared to those obtained in dependent mode. This shows that the house’s electrical installation has a good footprint in the input current signal.
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