dc.contributor.advisor | Ibrahim, Walid Morsi | |
dc.contributor.author | Gillis, Jessie Michael | |
dc.date.accessioned | 2017-04-28T19:37:24Z | |
dc.date.accessioned | 2022-03-29T16:33:28Z | |
dc.date.available | 2017-04-28T19:37:24Z | |
dc.date.available | 2022-03-29T16:33:28Z | |
dc.date.issued | 2016-10-01 | |
dc.identifier.uri | https://hdl.handle.net/10155/744 | |
dc.description.abstract | 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%. | en |
dc.description.sponsorship | University of Ontario Institute of Technology | en |
dc.language.iso | en | en |
dc.subject | NILM | en |
dc.subject | Wavelet design | en |
dc.subject | Load disaggregation | en |
dc.subject | Transient analysis | en |
dc.title | Time-frequency analysis techniques for non-intrusive load monitoring | en |
dc.type | Thesis | en |
dc.degree.level | Master of Applied Science (MASc) | en |
dc.degree.discipline | Electrical and Computer Engineering | en |