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Acoustic and device feature fusion for load recognition

Version 2 2024-06-04, 06:14
Version 1 2016-11-29, 14:15
conference contribution
posted on 2024-06-04, 06:14 authored by A Zoha, A Gluhak, M Nati, MA Imran, Sutharshan RajasegararSutharshan Rajasegarar
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.

History

Pagination

386-392

Location

Sofia, Bulgaria

Start date

2012-09-06

End date

2012-09-08

ISSN

1541-1672

eISSN

1941-1294

ISBN-13

9781467327824

Language

eng

Publication classification

E Conference publication, E1.1 Full written paper - refereed

Copyright notice

2012, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

IS 2012 : Intelligent systems: methodology, systems, applications in emerging technologies : Proceedings of the 2012 6th IEEE International Conference Intelligent Systems

Event

IEEE Computational Intelligence Society. Conference (6th : 2012 : Sofia, Bulgaria)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Computational Intelligence Society Conference