Deep online hierarchical dynamic unsupervised learning for pattern mining from utility usage data
Authors: Mohamad, S. and Bouchachia, A.
Journal: Neurocomputing
Volume: 390
Pages: 359-373
eISSN: 1872-8286
ISSN: 0925-2312
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.
https://eprints.bournemouth.ac.uk/32714/
Source: Scopus
Deep online hierarchical dynamic unsupervised learning for pattern mining from utility usage data
Authors: Mohamad, S. and Bouchachia, A.
Journal: NEUROCOMPUTING
Volume: 390
Pages: 359-373
eISSN: 1872-8286
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.08.093
https://eprints.bournemouth.ac.uk/32714/
Source: Web of Science (Lite)
Deep Online Hierarchical Dynamic Unsupervised Learning for Pattern Mining from Utility Usage Data
Authors: Mohammed, S. and Bouchachia, A.
Journal: Neurocomputing
Publisher: Elsevier
ISSN: 0925-2312
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.
https://eprints.bournemouth.ac.uk/32714/
Source: Manual
Deep Online Hierarchical Dynamic Unsupervised Learning for Pattern Mining from Utility Usage Data
Authors: Mohammed, S. and Bouchachia, A.
Journal: Neurocomputing
Volume: 390
Issue: May
Pages: 359-373
ISSN: 0925-2312
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.
https://eprints.bournemouth.ac.uk/32714/
Source: BURO EPrints