Ioshchikhes, Borys ; Piendl, Daniel ; Schmitz, Henrik ; Heiland, Jasper ; Weigold, Matthias
Hrsg.: Kohl, Holger ; Seliger, Günther ; Dietrich, Franz (2023)
Development of a Holistic Framework for Identifying Energy Efficiency Potentials of Production Machines.
18th Global Conference on Sustainable Manufacturing. Berlin (05.10.2022-07.10.2022)
doi: 10.1007/978-3-031-28839-5_48
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
A prerequisite to identify energy efficiency potentials and to improve energy efficiency is the measurement and analysis of the energy demand. However, in industrial practice, approaches to identify energy efficiency measures of production machines are associated with high costs for metering equipment and time consuming analysis requiring expertise. Against this background, this paper describes a comprehensive and cost-efficient framework from acquisition to analysis of energy data to serve as a starting point to increase energy efficiency in manufacturing. For this purpose, an energy transparency and analysis system is being developed that can measure, record and analyze electrical quantities. The validity of the data acquisition can be verified by utilizing a Raspberry Pi as a low-cost edge analyzer device. Measurement data is stored with associated metadata in a SQLite database for subsequent processing in a Python-based web application, in which machine learning algorithms can be deployed. The algorithms can be used to process vast amounts of data and to provide a basis for calculating energy performance indicators to reveal energy efficiency potentials. The overall workflow is validated using a lathe and a cleaning machine within the ETA Research Factory at the Technical University of Darmstadt.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Herausgeber: | Kohl, Holger ; Seliger, Günther ; Dietrich, Franz |
| Autor(en): | Ioshchikhes, Borys ; Piendl, Daniel ; Schmitz, Henrik ; Heiland, Jasper ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Development of a Holistic Framework for Identifying Energy Efficiency Potentials of Production Machines |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Cham |
| Verlag: | Springer International Publishing |
| Buchtitel: | Manufacturing driving circular economy : proceedings of the 18th Global Conference on Sustainable Manufacturing, October 5-7 2022, Berlin |
| Reihe: | Lecture Notes in Mechanical Engineering |
| Veranstaltungstitel: | 18th Global Conference on Sustainable Manufacturing |
| Veranstaltungsort: | Berlin |
| Veranstaltungsdatum: | 05.10.2022-07.10.2022 |
| DOI: | 10.1007/978-3-031-28839-5_48 |
| Kurzbeschreibung (Abstract): | A prerequisite to identify energy efficiency potentials and to improve energy efficiency is the measurement and analysis of the energy demand. However, in industrial practice, approaches to identify energy efficiency measures of production machines are associated with high costs for metering equipment and time consuming analysis requiring expertise. Against this background, this paper describes a comprehensive and cost-efficient framework from acquisition to analysis of energy data to serve as a starting point to increase energy efficiency in manufacturing. For this purpose, an energy transparency and analysis system is being developed that can measure, record and analyze electrical quantities. The validity of the data acquisition can be verified by utilizing a Raspberry Pi as a low-cost edge analyzer device. Measurement data is stored with associated metadata in a SQLite database for subsequent processing in a Python-based web application, in which machine learning algorithms can be deployed. The algorithms can be used to process vast amounts of data and to provide a basis for calculating energy performance indicators to reveal energy efficiency potentials. The overall workflow is validated using a lathe and a cleaning machine within the ETA Research Factory at the Technical University of Darmstadt. |
| Freie Schlagworte: | energy transparency; data acquisition; sustainable manufacturing |
| 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 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > TEC Fertigungstechnologie |
| Hinterlegungsdatum: | 02 Jun 2023 11:55 |
| Letzte Änderung: | 16 Jul 2024 08:54 |
| PPN: | 508364868 |
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