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Acoustic and Device Feature Fusion for Load Recognition

Zoha, A, Gluhak, A, Imran, MA, Nati, M and Rajasegarar, S (2012) Acoustic and Device Feature Fusion for Load Recognition In: in Proceedings of the 6th IEEE International Conference on Intelligent Systems (IEEE IS), 2012-09-06 - 2012-09-08, Sofia, Bulgaria.

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Abstract

Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multilayer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. The highest recognition performance however is shown by support vector machines, for the device and audio recognition experiments. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only.

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, AUNSPECIFIEDUNSPECIFIED
Gluhak, AUNSPECIFIEDUNSPECIFIED
Imran, MAUNSPECIFIEDUNSPECIFIED
Nati, MUNSPECIFIEDUNSPECIFIED
Rajasegarar, SUNSPECIFIEDUNSPECIFIED
Date : 22 October 2012
Identification Number : 10.1109/IS.2012.6335166
Contributors :
ContributionNameEmailORCID
PublisherIEEE, UNSPECIFIEDUNSPECIFIED
Additional Information : © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 19 Dec 2012 10:38
Last Modified : 23 Sep 2013 19:50
URI: http://epubs.surrey.ac.uk/id/eprint/735973

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