Deep online hierarchical unsupervised learning for pattern mining from utility usage data

This source preferred by Hamid Bouchachia

Authors: Mohamad, S., Arifoglu, D., Mansouri, C. and Bouchachia, A.

http://eprints.bournemouth.ac.uk/30874/

Start date: 5 September 2018

Journal: Advances in Computational Intelligence Systems

Volume: 840

Pages: 276-290

This source preferred by Hamid Bouchachia

Authors: Mohamad, S., Arifoglu, D., Mansouri, C. and Bouchachia, A.

http://eprints.bournemouth.ac.uk/30874/

Start date: 5 September 2018

This data was imported from Scopus:

Authors: Mohamad, S., Arifoglu, D., Mansouri, C. and Bouchachia, A.

http://eprints.bournemouth.ac.uk/30874/

Journal: Advances in Intelligent Systems and Computing

Volume: 840

Pages: 276-290

ISBN: 9783319979816

ISSN: 2194-5357

DOI: 10.1007/978-3-319-97982-3_23

© Springer Nature Switzerland AG 2019. Machine learning approaches for non-intrusive load monitoring (NILM) have focused on supervised algorithms. Unsupervised approaches can be more interesting and of more practical use in real case scenarios. More specifically, they do not require labelled training data to be collected from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Deep Belief network (DBN) and online Latent Dirichlet Allocation (LDA). Firstly, the raw signals of the house utilities are fed into DBN to extract low-level generic features in an unsupervised fashion, and then the hierarchical Bayesian model, LDA, learns high-level features that capture the correlations between the low-level ones. Thus, the proposed method (DBN-LDA) harnesses the DBN’s ability of learning distributed hierarchies of features to extract sophisticated appliances-specific features without the need of precise human-crafted input representations. The clustering power of the hierarchical Bayesian models helps further summarise the input data by extracting higher-level information representing the residents’ energy consumption patterns. Using Deep-Hierarchical models reduces the computational complexity since LDA is not directly applied to the raw data. The computational efficiency is crucial as our application involves massive data from different types of utility usages. Moreover, we develop a novel online inference algorithm to cope with this big data. Another novelty of this work is that the data is a combination of different utilities (e.g., electricity, water and gas) and some sensors measurements. Finally, we propose different methods to evaluate the results and preliminary experiments show that the DBN-LDA is promising to extract useful patterns.

This data was imported from Web of Science (Lite):

Authors: Mohamad, S., Arifoglu, D., Mansouri, C. and Bouchachia, A.

http://eprints.bournemouth.ac.uk/30874/

Journal: ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI)

Volume: 840

Pages: 276-290

eISSN: 2194-5365

ISBN: 978-3-319-97981-6

ISSN: 2194-5357

DOI: 10.1007/978-3-319-97982-3_23

The data on this page was last updated at 04:57 on June 24, 2019.