Acoustic and device feature fusion for load recognition Zoha, Ahmed, Gluhak, Alexander, Nati, Michele, Imran, Muhammad Ali and Rajasegarar, Sutharshan 2016, Acoustic and device feature fusion for load recognition. In Hadjiski, Mincho, Kasabov, Nikola, Filev, Dimitar and Jotsov, Vladimir (ed), Novel applications of intelligent systems, Springer, Berlin, Germany, pp.287-300, doi: 10.1007/978-3-319-14194-7_15. Attached Files Name Description MIMEType Size Downloads Title Acoustic and device feature fusion for load recognition Author(s) Zoha, Ahmed Gluhak, Alexander Nati, Michele Imran, Muhammad Ali Rajasegarar, Sutharshan Title of book Novel applications of intelligent systems Editor(s) Hadjiski, Mincho Kasabov, Nikola Filev, Dimitar Jotsov, Vladimir Publication date 2016 Series Studies in computational intelligence Chapter number 15 Total chapters 15 Start page 287 End page 300 Total pages 14 Publisher Springer Place of Publication Berlin, Germany Keyword(s) non-intrusive load monitoring (NILM) energy reduction energy monitoring audio features support vector machines (SVM) Science & Technology Technology Computer Science, Artificial Intelligence Robotics Computer Science Summary 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 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. ISBN 9783319141947 ISSN 1860-949X Language eng DOI 10.1007/978-3-319-14194-7_15 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1.1 Book chapter ERA Research output type B Book chapter Copyright notice ©2016, Springer International Publishing Switzerland Persistent URL http://hdl.handle.net/10536/DRO/DU:30083728 Document type: Book Chapter Collection: School of Information Technology Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 25 May 2016, 19:27:01 EST Thu, 26 May 2016, 05:02:57 EST Wed, 17 Aug 2016, 06:08:29 EST Thu, 15 Sep 2016, 13:08:51 EST Thu, 15 Sep 2016, 13:13:25 EST Thu, 15 Sep 2016, 13:15:05 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 73 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 25 May 2016, 19:27:01 EST

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