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dc.contributor.authorZink, Robin
dc.contributor.authorLademann, Tobias
dc.date.accessioned2025-03-28T09:44:37Z
dc.date.available2025-03-28T09:44:37Z
dc.date.issued2025-03-24
dc.identifier.urihttps://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4518
dc.descriptionHere you can find the supplementary material to the paper “An Empirical Comparison of Machine Learning Methods for Thermal Load Forecasting in Industrial Production Systems”: SampleMeasurementDataETAFactoryHNLTHNHT.csv (data set with measurement data of the thermal power of Heating Network Low Temperate and Heating Network High Temperature in kW, the mean ambient temperature of the next 48 hours in °C and the production state (no production | production) of the throughput parts cleaning machine in the ETA Factory).de_DE
dc.rightsCreative Commons Attribution 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectLoad forecastingde_DE
dc.subjectThermal energyde_DE
dc.subjectMachine Learningde_DE
dc.subjectIndustrial production systemsde_DE
dc.subject.classification4.22-01 Energieverfahrenstechnikde_DE
dc.subject.ddc620
dc.titleSupplementary Material | An Empirical Comparison of Machine Learning Methods for Thermal Load Forecasting in Industrial Production Systemsde_DE
dc.typeDatasetde_DE
tud.projectBund/BMBF | 03SFK3A0-3 | SynErgie3de_DE
tud.unitTUDa


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Creative Commons Attribution 4.0
Solange nicht anders angezeigt, wird die Lizenz wie folgt beschrieben: Creative Commons Attribution 4.0