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Filtering in non-Intrusive load monitoring

Thesis type
(Thesis) M.A.Sc.
Date created
2021-12-09
Authors/Contributors
Abstract
Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption. Non-intrusive load monitoring (NILM) is one name for this topic. One of the hardest problems NILM faces is the ability to run unsupervised – discovering appliances without prior knowledge – and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. This thesis showcases two filters that are used to denoise power signals, which results in better clustering accuracy for NILM event based methods. Both filters show to outperform a state-of-the-art denoising filter, in terms of run-time. A fully unsupervised NILM solution is presented, the algorithm is based on a hybrid knapsack problem with a Gaussian mixture model. Finally, a novel metric is developed to measure NILM disaggregation performance. The metric shows to be robust under a set of fundamental test cases.
Document
Identifier
etd21742
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Makonin, Stephen
Thesis advisor: Vaughan, Rodney
Language
English
Member of collection
Download file Size
input_data\21723\etd21742.pdf 3.33 MB

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