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