A graph-based signal processing approach for low-rate energy disaggregation

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

30 Citations (Scopus)
291 Downloads (Pure)

Abstract

Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches.
Original languageEnglish
Title of host publication2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Proceedings
Place of PublicationPiscataway, NJ
Pages81-87
Number of pages7
DOIs
Publication statusPublished - 9 Dec 2014
Event2014 IEEE Symposium Series on Computational Intelligence for Engineering Solutions (CIES) - FL, Orlando, United States
Duration: 9 Dec 201412 Dec 2014

Conference

Conference2014 IEEE Symposium Series on Computational Intelligence for Engineering Solutions (CIES)
Abbreviated titleCIES
CountryUnited States
CityOrlando
Period9/12/1412/12/14

Keywords

  • energy disaggregation
  • computational modeling
  • Hidden Markov models
  • image edge detection
  • microwave theory and techniques
  • noise
  • smoothing methods
  • testing

Fingerprint Dive into the research topics of 'A graph-based signal processing approach for low-rate energy disaggregation'. Together they form a unique fingerprint.

  • Projects

    Cite this

    Stankovic, V., Liao, J., & Stankovic, L. (2014). A graph-based signal processing approach for low-rate energy disaggregation. In 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Proceedings (pp. 81-87). https://doi.org/10.1109/CIES.2014.7011835