dc.contributor.advisor | Ibrahim, Walid Morsi | |
dc.contributor.author | Chung, Jefferson | |
dc.date.accessioned | 2017-04-20T19:08:11Z | |
dc.date.accessioned | 2022-03-29T16:41:17Z | |
dc.date.available | 2017-04-20T19:08:11Z | |
dc.date.available | 2022-03-29T16:41:17Z | |
dc.date.issued | 2016-10-01 | |
dc.identifier.uri | https://hdl.handle.net/10155/735 | |
dc.description.abstract | Non-intrusive load monitoring is the concept of determining the operational loads using
single-point sensing. The features contained within the electrical load’s signal are used to identify
a unique signature which is used by a machine learning classifier to automate the load
identification process. In this thesis, existing machine learning classification techniques are
reviewed within the context of the non-intrusive load monitoring application. A non-intrusive load
monitoring algorithm is developed in this to extract the prominent hidden features contained within
the electrical load’s signal which helps identify the operation of different appliances from a single
point of an electrical circuit. Decision tree and Naïve Bayes classifiers are used as the machine
learning classification technique to automate the load classification process. The co-testing of
machine learning classifiers was introduced in this work to improve the classification accuracy of
previously seen methods when applying the one-against-the-rest testing approach. When the
proposed NILM algorithm was applied to a real test system, a classification accuracy of 99.61%
for decision tree and 99.38% for Naïve Bayes was obtained. When compared to previous methods
in literature utilizing one-against-the-rest testing approach, a classification accuracy of 76.31% for
decision tree and 67.44% for Naïve Bayes was obtained. The results demonstrate the effectiveness
of the proposed non-intrusive load monitoring approach through the notable significant increase
in the observed classification accuracies. | en |
dc.description.sponsorship | University of Ontario Institute of Technology | en |
dc.language.iso | en | en |
dc.subject | Machine learning | en |
dc.subject | Non-intrusive load monitoring | en |
dc.subject | Co-testing | en |
dc.title | Machine learning classification 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 |