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Deep Online Hierarchical Dynamic Unsupervised Learning for Pattern Mining from Utility Usage Data.

Mohammed, S. and Bouchachia, A., 2020. Deep Online Hierarchical Dynamic Unsupervised Learning for Pattern Mining from Utility Usage Data. Neurocomputing. (In Press)

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DOI: 10.1016/j.neucom.2019.08.093

Abstract

While most non-intrusive load monitoring (NILM) work has focused on supervised algorithms, unsupervised approaches can be more interesting and practical. Specifically, they do not require labelled training data to be acquired from the individual appliances and can be deployed to operate on the measured aggregate data directly. We propose a fully unsupervised novel NILM framework based on Dynamic Bayesian hierarchical mixture model and Deep Belief network (DBN). The deep network learns, in unsupervised fashion, low-level generic appliance-specific features from the raw signals of the house utilities usage, then the hierarchical Bayesian model learns high-level features representing the consumption patterns of the residents captured by the correlations among the low-level features. The temporal ordering of the high-level features is captured by the Dynamic Bayesian Model. Using this architecture, we overcome the computational complexity that would occur if temporal modelling was directly applied to the raw data or even to the constructed features. The computational efficiency is crucial as our application involves massive data from different utilities usage. Moreover, we develop a novel online inference algorithm to cope with this big data. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns.

Item Type:Article
ISSN:0925-2312
Uncontrolled Keywords:Non-intrusive load monitoring ; Bayesian modelling ; Online learning ; Human activity recognition
Group:Faculty of Science & Technology
ID Code:32714
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:05 Sep 2019 09:02
Last Modified:06 Nov 2019 12:05

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