Non-intrusive appliance load monitoring using low-resolution smart meter data

Research output: Contribution to conferencePaperpeer-review

53 Citations (Scopus)

Abstract

We propose two algorithms for power load disaggregation at low-sampling rates (greater than 1sec): a low-complexity, supervised approach based on Decision Trees and an unsupervised method based on Dynamic Time Warping. Both proposed algorithms share common pre-classification steps. We provide reproducible algorithmic description and benchmark the proposed methods with a state-of-the-art Hidden Markov Model (HMM)-based approach. Experimental results using three US and three UK households, show that both proposed methods outperform the HMM-based approach and are capable of disaggregating a range of domestic loads even when the training period is very short.
Original languageEnglish
Pages535-540
Number of pages6
DOIs
Publication statusPublished - Nov 2014
EventIEEE International Conference on Smart Grid Communications - Venice, Italy
Duration: 3 Nov 20146 Nov 2014
http://sgc2014.ieee-smartgridcomm.org/

Conference

ConferenceIEEE International Conference on Smart Grid Communications
CountryItaly
CityVenice
Period3/11/146/11/14
Internet address

Keywords

  • decision trees
  • domestic appliances
  • hidden Markov models
  • load management
  • smart meters

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