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Unseen Appliances Identification

  • Antonio Ridi
  • Christophe Gisler
  • Jean Hennebert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

We assess the feasibility of unseen appliance recognition through the analysis of their electrical signatures recorded using low-cost smart plugs. By unseen, we stress that our approach focuses on the identification of appliances that are of different brands or models than the one in training phase. We follow a strictly defined protocol in order to provide comparable results to the scientific community. We first evaluate the drop of performance when going from seen to unseen appliances. We then analyze the results of different machine learning algorithms, as the k-Nearest Neighbor (k-NN) and Gaussian Mixture Models (GMMs). Several tunings allow us to achieve 74% correct accuracy using GMMs which is our current best system.

Keywords

Intrusive Load Monitoring (ILM) appliance recognition electric signatures load identification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Antonio Ridi
    • 1
    • 2
  • Christophe Gisler
    • 1
    • 2
  • Jean Hennebert
    • 1
    • 2
  1. 1.College of Engineering and Architecture of Fribourg, ICT InstituteUniversity of Applied Sciences Western SwitzerlandSwitzerland
  2. 2.Department of InformaticsUniversity of FribourgFribourgSwitzerland

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