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Hybrid Virtual Energy Metering Points — A Low-Cost Energy Monitoring Approach for Production Systems Based on Offline Trained Prediction Models

Sossenheimer, Johannes ; Vetter, Oliver ; Abele, Eberhard ; Weigold, Matthias (2020)
Hybrid Virtual Energy Metering Points — A Low-Cost Energy Monitoring Approach for Production Systems Based on Offline Trained Prediction Models.
In: Procedia CIRP, 93
doi: 10.1016/j.procir.2020.04.128
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

With the ongoing digitalization of industrial production, innovative ways of creating energy transparency on the shop floor are emerging. Virtual energy metering points, which use process data to predict the energy and resource demand, enable a cost-effective increase in energy transparency on machine level. In this paper, an approach based on offline trained neural networks is presented, through which the energy and resource consumption is continuously predicted for various production systems on machine and component level with high accuracy. Also the necessary data availability and the transferability to processes that are not included in the training dataset are discussed.

Typ des Eintrags: Artikel
Erschienen: 2020
Autor(en): Sossenheimer, Johannes ; Vetter, Oliver ; Abele, Eberhard ; Weigold, Matthias
Art des Eintrags: Bibliographie
Titel: Hybrid Virtual Energy Metering Points — A Low-Cost Energy Monitoring Approach for Production Systems Based on Offline Trained Prediction Models
Sprache: Englisch
Publikationsjahr: 2020
Verlag: Elsevier B.V.
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Procedia CIRP
Jahrgang/Volume einer Zeitschrift: 93
DOI: 10.1016/j.procir.2020.04.128
Kurzbeschreibung (Abstract):

With the ongoing digitalization of industrial production, innovative ways of creating energy transparency on the shop floor are emerging. Virtual energy metering points, which use process data to predict the energy and resource demand, enable a cost-effective increase in energy transparency on machine level. In this paper, an approach based on offline trained neural networks is presented, through which the energy and resource consumption is continuously predicted for various production systems on machine and component level with high accuracy. Also the necessary data availability and the transferability to processes that are not included in the training dataset are discussed.

Freie Schlagworte: Energy transparency, virtual energy metering, energy monitoring, metalworking industry
Fachbereich(e)/-gebiet(e): 16 Fachbereich Maschinenbau
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW)
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > ETA Energietechnologien und Anwendungen in der Produktion
Hinterlegungsdatum: 22 Dez 2020 11:06
Letzte Änderung: 22 Dez 2020 11:06
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