Mohamad, S. and Bouchachia, A., 2020. Online Gaussian LDA for Unsupervised Pattern Mining from Utility Usage Data. In: 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 14-17 December 2020, Miami, FL, USA, 41 - 48.
Full text available as:
|
PDF
09356246.pdf - Published Version 1MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
DOI: 10.1109/ICMLA51294.2020.00016
Abstract
Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While most work has focused on supervised algorithms, unsupervised approaches can be more interesting and of practical use in real case scenarios. Specifically, they do not require labelled training data to be acquired 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 Bayesian hierarchical mixture models. In particular, we develop a new method based on Gaussian Latent Dirichlet Allocation (GLDA) in order to extract global components that summarise the energy signal. These components provide a representation of the consumption patterns. Designed to cope with big data, our algorithm, unlike existing NILM ones, does not focus on appliance recognition. To handle this massive data, GLDA works online. Another novelty of this work compared to the existing NILM is that the data involves different utilities (e.g, electricity, water and gas) as well as some sensors measurements. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Group: | Faculty of Science & Technology |
ID Code: | 35315 |
Deposited By: | Symplectic RT2 |
Deposited On: | 23 Mar 2021 13:16 |
Last Modified: | 14 Mar 2022 14:26 |
Downloads
Downloads per month over past year
Repository Staff Only - |