Weber, Thomas ; Sossenheimer, Johannes ; Schäfer, Steffen ; Ott, Moritz ; Walther, Jessica ; Abele, Eberhard (2019)
Machine Learning based System Identification Tool for data-based Energy and Resource Modeling and Simulation.
In: Procedia CIRP, 26th CIRP Conference on Life Cycle Engineering (LCE) Purdue University, West Lafayette, IN (USA), 80
doi: 10.1016/j.procir.2018.12.021
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Generated machine data is often not fully utilized in modern power production, although it could provide new approaches to significantly increase productivity, flexibility and resource efficiency as well as energy efficiency in production. Data-based models, which can be created with the help of machine learning algorithms, can map the system's behavior accurately and thus provide a basis for a better system understanding for further energy und resource optimization approaches. The objective of this paper is to develop a generic system identification tool that uses the above-mentioned data-based modeling approach to optimize the electrical power and resource consumption for a given load, regardless of the considered plant or machine. Therefore, the system identification tool autonomously preprocesses the data, compares different hyperparameters for neural networks to reproduce the system's behavior and finally selects the best-suited regression algorithm with the corresponding hyperparameters.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Weber, Thomas ; Sossenheimer, Johannes ; Schäfer, Steffen ; Ott, Moritz ; Walther, Jessica ; Abele, Eberhard |
| Art des Eintrags: | Bibliographie |
| Titel: | Machine Learning based System Identification Tool for data-based Energy and Resource Modeling and Simulation |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP, 26th CIRP Conference on Life Cycle Engineering (LCE) Purdue University, West Lafayette, IN (USA) |
| Jahrgang/Volume einer Zeitschrift: | 80 |
| DOI: | 10.1016/j.procir.2018.12.021 |
| Kurzbeschreibung (Abstract): | Generated machine data is often not fully utilized in modern power production, although it could provide new approaches to significantly increase productivity, flexibility and resource efficiency as well as energy efficiency in production. Data-based models, which can be created with the help of machine learning algorithms, can map the system's behavior accurately and thus provide a basis for a better system understanding for further energy und resource optimization approaches. The objective of this paper is to develop a generic system identification tool that uses the above-mentioned data-based modeling approach to optimize the electrical power and resource consumption for a given load, regardless of the considered plant or machine. Therefore, the system identification tool autonomously preprocesses the data, compares different hyperparameters for neural networks to reproduce the system's behavior and finally selects the best-suited regression algorithm with the corresponding hyperparameters. |
| Freie Schlagworte: | Energy efficiency; Ressource efficiency; System Identification; Machine Learning |
| 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: | 27 Aug 2019 05:31 |
| Letzte Änderung: | 27 Aug 2020 09:24 |
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