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Acoustic and device feature fusion for load recognition
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posted on 2016-01-28, 00:00 authored by A Zoha, A Gluhak, M Nati, M A Imran, Sutharshan RajasegararSutharshan RajasegararAppliance-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 multi-layer 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. 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
Title of book
Novel applications of intelligent systemsVolume
586Series
Studies in computational intelligenceChapter number
15Pagination
287 - 300Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
1860-949XISBN-13
9783319141947Language
engPublication classification
B Book chapter; B1.1 Book chapterCopyright notice
2016, Springer International Publishing SwitzerlandExtent
15Editor/Contributor(s)
M Hadjiski, N Kasabov, D Filev, V JotsovUsage metrics
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