Decomposition Techniques for Non-intrusive Home Appliance Load Monitoring

• Author / Creator

  Tabatabaei, Seyed Mostafa

• Energy-saving is a key element of Smart Grid. By encouraging consumers to
  moderate their energy demands, utilities can make more efficient use of their
  generation assets, and reduce total fuel consumption. For this purpose, we must
  provide homeowners with appliance energy consumption data, without requiring
  sensors on each appliance. This means that energy consumption from the house
  main feeder must be disaggregated into individual appliances.
  In this thesis, two novel methodologies for disaggregating household power
  consumption are evaluated. The first method is multi-label classification, which is
  used to predict appliance participation in the power signal. The second method is
  a new signature-based sequence matching algorithm. Two sets of features have
  been used. In the time domain, a delay embedding of the observed power signal is
  constructed. The second feature set is a wavelet decomposition of the power
  signal, using Haar wavelet. We evaluate our techniques and features on two
  synthetic datasets, and two households from REDD.

• Subjects / Keywords

  • Delay Coordinate embedding
  • Multi-label Classification
  • Sequence Matching
  • Non-intrusive load monitoirng

• Graduation date

  Spring 2014

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3Z60C95M

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.