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Deep learning applications in non-intrusive load monitoring

Thesis type
(Thesis) M.A.Sc.
Date created
2020-08-19
Authors/Contributors
Author: Harell, Alon
Abstract
Non-Intrusive Load Monitoring (NILM) is a technique for inferring the power consumption of each appliance within a home from one central meter, aiding in energy conservation. In this thesis I present several Deep Learning solutions for NILM, starting with two preliminary works – A proof of concept project for multisensory NILM on a Raspberry Pi; and a fully developed NILM solution named WaveNILM. Despite their success, both methods struggled to generalize outside their training data, a common problem in NILM. To improve generalization, I designed a framework for synthesizing truly novel appliance level power signatures based on generative adversarial networks (GAN) – the main project of this thesis. This generator, named PowerGAN, is trained using a variety of GAN techniques. I present a comparison of PowerGAN to other data synthesis work in the context of NILM and demonstrate that PowerGAN is able to create truly synthetic, realistic, diverse, appliance power signatures.
Document
Identifier
etd21013
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: Bajic, Ivan V.
Language
English
Member of collection
Download file Size
input_data\21958\etd21013.pdf 6.69 MB

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