• search hit 2 of 4
Back to Result List

Measurement system and dataset for in-depth analysis of appliance energy consumption in industrial environment

  • To support a rational and efficient use of electrical energy in residential and industrial environments, Non-Intrusive Load Monitoring (NILM) provides several techniques to identify state and power consumption profiles of connected appliances. Design requirements for such systems include a low hardware and installations costs for residential, reliability and high-availability for industrial purposes, while keeping invasive interventions into the electrical infrastructure to a minimum. This work introduces a reference hardware setup that allows an in depth analysis of electrical energy consumption in industrial environments. To identify appliances and their consumption profile, appropriate identification algorithms are developed by the NILM community. To enable an evaluation of these algorithms on industrial appliances, we introduce the Laboratory-measured IndustriaL Appliance Characteristics (LILAC) dataset: 1302 measurements from one, two, and three concurrently running appliances of 15 appliance types, measured with the introduced testbed. To allow in-depth appliance consumption analysis, measurements were carried out with a sampling rate of 50 kHz and 16-bit amplitude resolution for voltage and current signals. We show in experiments that signal signatures, contained in the measurement data, allows one to distinguish the single measured electrical appliances with a baseline machine learning approach of nearly 100% accuracy.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Matthias KahlORCiD, Veronika Krause, Rudolf Hackenberg, Anwar Ul Haq, Anton Horn, Hans-Arno Jacobsen, Thomas Kriechbaumer, Michael Petzenhauser, Mikhail Shamonin (Chamonine)ORCiDGND, Anton Udalzow
DOI:https://doi.org/10.1515/teme-2018-0038
Parent Title (German):tm - Technisches Messen
Publisher:De Gruyter
Document Type:Article
Language:English
Year of first Publication:2019
Release Date:2022/02/02
Tag:DAQ; NILM; appliance monitoring
Volume:86
Issue:1
First Page:1
Last Page:13
Institutes:Fakultät Elektro- und Informationstechnik
Fakultät Informatik und Mathematik
Fakultät Elektro- und Informationstechnik / Labor Intelligente Materialien und Strukturen
Fakultät Informatik und Mathematik / Labor Informationssicherheit und Complience (ISC)
Begutachtungsstatus:peer-reviewed
research focus:Energie und Mobilität
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG