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Combining several distinct electrical features to enhance nonintrusive load monitoring

: Bernard, Timo; Wohland, Daniel Michael; Klaaßen, Julian; Bögel, Gerd vom


Institute of Electrical and Electronics Engineers -IEEE-:
International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2015 : October 20-23, 2015, Offenburg University of Applied Sciences, Germany; Proceedings
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4673-8732-3 (Print)
ISBN: 978-1-4673-8734-7
International Conference on Smart Grid and Clean Energy Technologies (ICSGCE) <2015, Offenburg>
Conference Paper
Fraunhofer IMS ()
nonintrusive load monitoring; load disaggregation; smart metering; device identification; electrical features; energy efficiency; energy consumption; load management; unsupervised learning

Smart meters are state of the art for electricity measurement in domestic and commercial buildings. So far they are only able to track the overall electricity consumption, though appliance specific feedback can lead to substantial higher energy savings. One promising option to reach appliance specific consumption information is nonintrusive load monitoring (NILM), in which this information is gained by disaggregating the overall load profile from a single-point measurement. To improve the accuracy of NILM, in this paper we investigate several distinct electrical features and combine them in an unsupervised learning algorithm. Our algorithm evaluation shows promising results for this method.