Zink, Robin ; Ioshchikhes, Borys ; Weigold, Matthias (2024)
Concept drift monitoring for industrial load forecasting with artificial neural networks.
In: Procedia CIRP, 130
doi: 10.1016/j.procir.2024.10.065
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Long Short-Term Memory (LSTM) models are frequently applied for industrial energy load forecasting. However, real-world production systems are highly dynamic which poses major challenges. Concept drifts potentially lead to performance degradation that affects systems optimization for the worse. In this work, Concept Drift Detection (CDD) for industrial energy load forecasting with LSTM models is researched. For this purpose, five CDD algorithms are evaluated using the active power of a machine tool. Drift Detection Method (DDM) and Kolmogorov-Smirnov Windowing (KSWIN) proved to be particularly effective with easily interpretable and reasonable hyperparameters.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Zink, Robin ; Ioshchikhes, Borys ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Concept drift monitoring for industrial load forecasting with artificial neural networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2024 |
| Ort: | Amsterdam |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 130 |
| DOI: | 10.1016/j.procir.2024.10.065 |
| Kurzbeschreibung (Abstract): | Long Short-Term Memory (LSTM) models are frequently applied for industrial energy load forecasting. However, real-world production systems are highly dynamic which poses major challenges. Concept drifts potentially lead to performance degradation that affects systems optimization for the worse. In this work, Concept Drift Detection (CDD) for industrial energy load forecasting with LSTM models is researched. For this purpose, five CDD algorithms are evaluated using the active power of a machine tool. Drift Detection Method (DDM) and Kolmogorov-Smirnov Windowing (KSWIN) proved to be particularly effective with easily interpretable and reasonable hyperparameters. |
| Freie Schlagworte: | Artificial Intelligence, continual learning, energy, factory, machine learning, 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 |
| Hinterlegungsdatum: | 29 Nov 2024 08:32 |
| Letzte Änderung: | 29 Nov 2024 08:32 |
| PPN: | 524286620 |
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