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Harell, Alon - Deep learning applications in non-intrusive load monitoring

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This thesis has been submitted to the Library for purposes of graduation, but needs to be audited for technical details related to publication in order to be approved for inclusion in the Library collection.

Term : Summer 2020

Degree : M.A.Sc.

Degree type : Thesis

Department : School of Engineering Science

Faculty : Applied Sciences

Senior supervisor : Ivan V. Bajic

Thesis title : Deep learning applications in non-intrusive load monitoring

Given names : Alon

Surname : Harell

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.

Keywords : Deep learning; generative adversarial networks; NILM; load disaggregation;sustainability; neural networks

Total pages : 80