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  4. Non-Intrusive Load Monitoring (NILM): Unsupervised machine learning and feature fusion
 
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2018
Conference Paper
Title

Non-Intrusive Load Monitoring (NILM): Unsupervised machine learning and feature fusion

Title Supplement
Energy management for private and industrial applications
Abstract
Energy savings are an important building block for the clean energy transition. Studies show that the consideration of overall load profiles is not sufficient to identify significant saving potentials - as is the case with smart meters. Nonintrusive Load Monitoring enables a device specific consumption disaggregation in a cost effective way. Our work focuses on the fusion of low, mid and high frequency features which can enhance the disaggregation performance. Furthermore our suggested approach consists of an unsupervised machine learning technique which enables novelty detection, a small training phase and live processing. We conclude this paper with the algorithm evaluation on household and industrial datasets.
Author(s)
Bernard, Timo
Fraunhofer-Institut für Mikroelektronische Schaltungen und Systeme IMS  
Verbunt, Martin
Fraunhofer-Institut für Mikroelektronische Schaltungen und Systeme IMS  
Bögel, Gerd vom  
Fraunhofer-Institut für Mikroelektronische Schaltungen und Systeme IMS  
Wellmann, Thorsten
Uni Frankfurt am Main
Mainwork
International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2018  
Conference
International Conference on Smart Grid and Clean Energy Technologies (ICSGCE) 2018  
DOI
10.1109/ICSGCE.2018.8556735
Language
English
Fraunhofer-Institut für Mikroelektronische Schaltungen und Systeme IMS  
Keyword(s)
  • device identification

  • energy consumption

  • energy efficiency

  • high frequency electrical feature

  • load disaggregation

  • Nonintrusive Load Monitoring (NILM)

  • smart metering

  • unsupervised machine learning

  • load management

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