Dietrich, Bastian ; Walther, Jessica ; Chen, Yurui ; Weigold, Matthias (2021)
A Deep Learning Approach to Electric Load Forecasting of Machine Tools.
In: MM Science Journal, (5)
doi: 10.17973/MMSJ.2021_11_2021146
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The ongoing climate change and increasingly strict climate goals of the European Union demand decisive action in all sectors. Especially in manufacturing industry, demand response measures have a high potential to balance the industrial electricity consumption with the increasingly volatile electricity supply from renewable sources. This work aims to develop a method to forecast the electrical energy demand of metal cutting machine tools as a necessary input for implementing demand response measures in factories. Building on the results of a previous study, long short-term memory networks (LSTM) and convolutional neural networks (CNN) are examined in their performance for forecasting the electric load of a machine tool for a 100 second time horizon. The results show that especially the combination of CNN and LSTM in a deep learning approach generates accurate and robust time series forecasts with reduced feature preparation effort. To further improve the forecasting accuracy, different network architectures including an attention mechanism for the LSTMs and different hyperparameter combinations are evaluated. The results are validated on real production data obtained in the ETA Research Factory.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Dietrich, Bastian ; Walther, Jessica ; Chen, Yurui ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | A Deep Learning Approach to Electric Load Forecasting of Machine Tools |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | MM Science Journal |
| (Heft-)Nummer: | 5 |
| DOI: | 10.17973/MMSJ.2021_11_2021146 |
| Kurzbeschreibung (Abstract): | The ongoing climate change and increasingly strict climate goals of the European Union demand decisive action in all sectors. Especially in manufacturing industry, demand response measures have a high potential to balance the industrial electricity consumption with the increasingly volatile electricity supply from renewable sources. This work aims to develop a method to forecast the electrical energy demand of metal cutting machine tools as a necessary input for implementing demand response measures in factories. Building on the results of a previous study, long short-term memory networks (LSTM) and convolutional neural networks (CNN) are examined in their performance for forecasting the electric load of a machine tool for a 100 second time horizon. The results show that especially the combination of CNN and LSTM in a deep learning approach generates accurate and robust time series forecasts with reduced feature preparation effort. To further improve the forecasting accuracy, different network architectures including an attention mechanism for the LSTMs and different hyperparameter combinations are evaluated. The results are validated on real production data obtained in the ETA Research Factory. |
| Freie Schlagworte: | Machine Learning, Machine tool, Load forecasting |
| Zusätzliche Informationen: | Special Issue: HSM 2021 ; 16th International Conference on High Speed Machining ; October 26-27, 2021, Darmstadt, Germany |
| 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: | 08 Dez 2021 07:41 |
| Letzte Änderung: | 26 Jan 2022 10:31 |
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