Grosch, Benedikt Emanuel ; Ranzau, Heiko ; Dietrich, Bastian ; Kohne, Thomas ; Fuhrländer-Völker, Daniel ; Sossenheimer, Johannes ; Lindner, Martin ; Weigold, Matthias (2024)
A framework for researching energy optimization of factory operations.
In: Energy Informatics, 2022, 5 (Suppl 1)
doi: 10.26083/tuprints-00026611
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
Energy optimization of factory operations has gained increasing importance over recent years since it is understood as one way to counteract climate change. At the same time, the number of research teams working on energy-optimized factory operations has also increased. While many tools are useful in this area, our team has recognized the importance of a comprehensive framework to combine functionality for optimization, simulation, and communication with devices in the factory. Therefore, we developed a framework that provides a standardized interface to research energy-optimized factory operations with a rolling horizon approach. The optimization part of the framework is based on the OpenAI gym environment. The framework also provides connectors for multiple communication protocols including Open Platform Communication Unified Architecture and Modbus via Transmission Control Protocol. These facilities can be utilized to implement rolling horizon optimizations for factory systems easily and directly control devices in the factory with the optimization results. In this article, we present the framework and show some examples to prove the effectiveness of our approach.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Grosch, Benedikt Emanuel ; Ranzau, Heiko ; Dietrich, Bastian ; Kohne, Thomas ; Fuhrländer-Völker, Daniel ; Sossenheimer, Johannes ; Lindner, Martin ; Weigold, Matthias |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | A framework for researching energy optimization of factory operations |
| Sprache: | Englisch |
| Publikationsjahr: | 10 September 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Ort der Erstveröffentlichung: | Cham |
| Verlag: | Springer Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Energy Informatics |
| Jahrgang/Volume einer Zeitschrift: | 5 |
| (Heft-)Nummer: | Suppl 1 |
| Kollation: | 13 Seiten |
| DOI: | 10.26083/tuprints-00026611 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/26611 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Energy optimization of factory operations has gained increasing importance over recent years since it is understood as one way to counteract climate change. At the same time, the number of research teams working on energy-optimized factory operations has also increased. While many tools are useful in this area, our team has recognized the importance of a comprehensive framework to combine functionality for optimization, simulation, and communication with devices in the factory. Therefore, we developed a framework that provides a standardized interface to research energy-optimized factory operations with a rolling horizon approach. The optimization part of the framework is based on the OpenAI gym environment. The framework also provides connectors for multiple communication protocols including Open Platform Communication Unified Architecture and Modbus via Transmission Control Protocol. These facilities can be utilized to implement rolling horizon optimizations for factory systems easily and directly control devices in the factory with the optimization results. In this article, we present the framework and show some examples to prove the effectiveness of our approach. |
| Freie Schlagworte: | Industrial internet of things, Industrial demand side integration, Rolling horizon optimization |
| ID-Nummer: | 29 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-266117 |
| Zusätzliche Informationen: | The authors gratefully acknowledge fnancial support of the Project “KI4ETA” (Grant Number 03EN2053A) by the German Federal Ministry for Economic Afairs. |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 650 Management |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) |
| Hinterlegungsdatum: | 10 Sep 2024 07:37 |
| Letzte Änderung: | 11 Sep 2024 08:45 |
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| Suche nach Titel in: | TUfind oder in Google |
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