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

Energy management for private and industrial applications
: Bernard, Timo; Verbunt, Martin; Bögel, Gerd vom; Wellmann, Thorsten


Institute of Electrical and Electronics Engineers -IEEE-:
International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2018 : 29 May - 1 June, 2018, Kajang, Malaysia
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-6410-0
International Conference on Smart Grid and Clean Energy Technologies (ICSGCE) <7, 2018, Kajang/Malaysia>
Fraunhofer IMS ()
device identification; energy consumption; energy efficiency; high frequency electrical feature; load disaggregation; Nonintrusive Load Monitoring (NILM); smart metering; unsupervised machine learning; load management

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.