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Supplementary Material | Continual learning for very short-term load forecasting: A Case Study on parts cleaning

dc.contributor.authorMagin, Jonathan
dc.contributor.authorZink, Robin
dc.contributor.authorLuu, Daniel
dc.contributor.authorBaumert, Celina
dc.contributor.authorTettschlag, Nils
dc.date.accessioned2025-04-04T13:15:28Z
dc.date.available2025-04-04T13:15:28Z
dc.date.issued2024-07
dc.identifier.urihttps://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4537
dc.descriptionHere you can find the supplementary material to the paper "Continual learning for very short-term load forecasting: A Case Study on parts cleaning". Experiments were carried out on an industrial Throughput Parts Cleaning Machine (TPCM) at the ETA factory. The machine is equipped with power measurements (1 Hz) on electrical component level. The Dataset contains 1 min averaged data of sensor and control values. Additional informations are provided as text protocol.de_DE
dc.language.isoende_DE
dc.rightsCreative Commons Attribution 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectLoad forecastingde_DE
dc.subjectparts cleaningde_DE
dc.subjectMachine Learningde_DE
dc.subject.classification4.43-04 Künstliche Intelligenz und Maschinelle Lernverfahrende_DE
dc.subject.ddc004
dc.titleSupplementary Material | Continual learning for very short-term load forecasting: A Case Study on parts cleaningde_DE
dc.typeDatasetde_DE
dc.typeTextde_DE
tud.projectBund/BMBF | 03SFK3A0-3 | SynErgie3de_DE
tud.unitTUDa


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Creative Commons Attribution 4.0
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0