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A Comparison of Generative and Discriminative Appliance Recognition Models for Load Monitoring

Zoha, A., Imran, M. A., Gluhak, A. and Nati, M. (2013) A Comparison of Generative and Discriminative Appliance Recognition Models for Load Monitoring In: 1st International Conference on Sensing for Industry, Control, Communications, & Security Technologies (ICSICCST 2013), 24–26 June 2013, Indus University, Pakistan.

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Abstract

Appliance-level Load Monitoring (ALM) is essential, not only to optimize energy utilization, but also to promote energy awareness amongst consumers through real-time feedback mechanisms. Non-intrusive load monitoring is an attractive method to perform ALM that allows tracking of appliance states within the aggregated power measurements. It makes use of generative and discriminative machine learning models to perform load identification. However, particularly for low-power appliances, these algorithms achieve sub-optimal performance in a real world environment due to ambiguous overlapping of appliance power features. In our work, we report a performance comparison of generative and discriminative Appliance Recognition (AR) models for binary and multi-state appliance operations. Furthermore, it has been shown through experimental evaluations that a significant performance improvement in AR can be achieved if we make use of acoustic information generated as a by-product of appliance activity. We demonstrate that our a discriminative model FF-AR trained using a hybrid feature set which is a catenation of audio and power features improves the multi-state AR accuracy up to 10 %, in comparison to a generative FHMM-AR model.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Authors :
AuthorsEmailORCID
Zoha, A.UNSPECIFIEDUNSPECIFIED
Imran, M. A.UNSPECIFIEDUNSPECIFIED
Gluhak, A.UNSPECIFIEDUNSPECIFIED
Nati, M.UNSPECIFIEDUNSPECIFIED
Date : 2013
Contributors :
ContributionNameEmailORCID
PublisherIOP Publishing, UNSPECIFIEDUNSPECIFIED
Additional Information : Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Depositing User : Symplectic Elements
Date Deposited : 26 Jan 2016 11:54
Last Modified : 26 Jan 2016 11:54
URI: http://epubs.surrey.ac.uk/id/eprint/809750

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