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A Power Disaggregation Approach for fine-grained Machine Energy Monitoring by System Identification

Panten, Niklas ; Abele, Eberhard ; Schweig, Stefano :
A Power Disaggregation Approach for fine-grained Machine Energy Monitoring by System Identification.
[Online-Edition: http://dx.doi.org/10.1016/j.procir.2016.03.025]
In: 23rd CIRP Conference on Life Cycle Engineering, 48 pp. 325-330.
[Artikel], (2016)

Offizielle URL: http://dx.doi.org/10.1016/j.procir.2016.03.025

Kurzbeschreibung (Abstract)

Energy monitoring is one major prerequisite for energy efficiency measures. Energy and power data throughout different levels of production allow benchmarking and condition monitoring applications based on insightful energy performance indicators. However, fine-grained measurement concepts for energy and power require high investments with uncertain benefits. This paper presents a low-cost approach to monitor the component-by-component energy consumption with a minimum of sensor technology that can be applied to a variety of production machines. Aggregated energy data combined with components’ control signals are the basis for the determination of components’ energy consumptions using two system identification algorithms. While one method is realized in an offline-mode after data collection, the second approach utilizes real-time data based on a recursive least squares algorithm. Eventually, the feasibility of the theoretical system identification concepts is shown in a laboratory environment.

Typ des Eintrags: Artikel
Erschienen: 2016
Autor(en): Panten, Niklas ; Abele, Eberhard ; Schweig, Stefano
Titel: A Power Disaggregation Approach for fine-grained Machine Energy Monitoring by System Identification
Sprache: Englisch
Kurzbeschreibung (Abstract):

Energy monitoring is one major prerequisite for energy efficiency measures. Energy and power data throughout different levels of production allow benchmarking and condition monitoring applications based on insightful energy performance indicators. However, fine-grained measurement concepts for energy and power require high investments with uncertain benefits. This paper presents a low-cost approach to monitor the component-by-component energy consumption with a minimum of sensor technology that can be applied to a variety of production machines. Aggregated energy data combined with components’ control signals are the basis for the determination of components’ energy consumptions using two system identification algorithms. While one method is realized in an offline-mode after data collection, the second approach utilizes real-time data based on a recursive least squares algorithm. Eventually, the feasibility of the theoretical system identification concepts is shown in a laboratory environment.

Titel der Zeitschrift, Zeitung oder Schriftenreihe: 23rd CIRP Conference on Life Cycle Engineering
Band: 48
Verlag: Elsevier B.V.
Freie Schlagworte: Energy Efficiency; Production Machines; Energy Monitoring; Non-Intrusive Load Monitoring; Power Disaggregation; System Identification
Fachbereich(e)/-gebiet(e): 16 Fachbereich Maschinenbau
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement, Technologie und Werkzeugmaschinen (PTW)
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement, Technologie und Werkzeugmaschinen (PTW) > Umweltgerechte Produktion
Veranstaltungstitel: 23rd CIRP Conference on Life Cycle Engineering, Published by Elsevier B.V., 22.-24. Mai 2016
Hinterlegungsdatum: 26 Okt 2016 13:34
Offizielle URL: http://dx.doi.org/10.1016/j.procir.2016.03.025
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