You are not logged in.

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
Computer Science, Artificial Intelligence
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

Document type: Book Chapter
Collection: School of Information Technology
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 82 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Wed, 25 May 2016, 19:27:01 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact