Walther, Jessica ; Dietrich, Bastian ; Abele, Eberhard (2019)
Generic Machine Learning Approach for Very Short Term Load Forecasting of Production Machines.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
With the ongoing digitalization of industrial production, an increasing number of energy measuring points are installed in manufacturing environments which enable promising use cases for machine learning applications. This paper presents a generic machine learning approach to forecast the very short term load of production machines which can be utilized as decision support basis for control schemes and measures to decrease energy costs. The presented approach is developed and evaluated on production machines of the ETA research factory at the Technische Universität Darmstadt. The results indicate that the developed approach is feasible and creates a precise very short term load forecasting model for different production machines.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Walther, Jessica ; Dietrich, Bastian ; Abele, Eberhard |
| Art des Eintrags: | Bibliographie |
| Titel: | Generic Machine Learning Approach for Very Short Term Load Forecasting of Production Machines |
| Sprache: | Englisch |
| Publikationsjahr: | 12 August 2019 |
| Ort: | Västerås (Sweden) |
| Buchtitel: | Proceedings of 11th International Conference on Applied Energy, August 12-15 |
| Kurzbeschreibung (Abstract): | With the ongoing digitalization of industrial production, an increasing number of energy measuring points are installed in manufacturing environments which enable promising use cases for machine learning applications. This paper presents a generic machine learning approach to forecast the very short term load of production machines which can be utilized as decision support basis for control schemes and measures to decrease energy costs. The presented approach is developed and evaluated on production machines of the ETA research factory at the Technische Universität Darmstadt. The results indicate that the developed approach is feasible and creates a precise very short term load forecasting model for different production machines. |
| Freie Schlagworte: | Load forecasting, machine learning, feature selection, feature engineering, artificial neural networks |
| 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: | 24 Apr 2020 12:38 |
| Letzte Änderung: | 26 Jan 2022 10:33 |
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