Time-frequency analysis techniques for non-intrusive load monitoring

The work in this thesis examines time-frequency analysis techniques and in particular the wavelet transform to extract the features contained within the electrical load signals. A novel approach that is based on wavelet design was utilized to generate a wavelet library which was used to match each load signal to a specific wavelet using Procrustes and covariance analysis. In order to automate the load identification process, two machine learning classifiers representing an eager learner and a lazy learner were used in this work. The proposed wavelet design concept has been verified experimentally, and the results of implementing the proposed load detection and classification approach shows significant improvement in the classification accuracy compared to other existing detection approaches reaching an overall accuracy of 98%.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Gillis, Jessie Michael

Advisor dc:contributor.advisor

• Ibrahim, Walid Morsi

• NILM
• Wavelet design
• Load disaggregation
• Transient analysis

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/744

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/744