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Open Access

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

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Files

Protocol_SampleDataCL4CleaningMachines.txt (5.09 KB)
 SampleDataCL4CleaningMachines.csv (29.27 MB)

Date

2025-04-04

Type

Dataset
Text

Authors

Magin, Jonathan
Zink, Robin
Luu, Daniel
Baumert, Celina
Tettschlag, Nils

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

Here 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.

Keywords

Load forecasting, parts cleaning, Machine Learning

Citation

Identifier

https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4537

Endorsement

DFG Classification

4.43-04 Künstliche Intelligenz und Maschinelle Lernverfahren

Project(s)

Bund/BMBF | 03SFK3A0-3 | SynErgie3

Faculty

Collections

License

CC BY 4.0 - Attribution 4.0 International
Except where otherwise noted, this license is described as CC BY 4.0 - Attribution 4.0 International