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 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum