Supplementary Material | An Empirical Comparison of Machine Learning Methods for Thermal Load Forecasting in Industrial Production Systems

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dc.contributor.author Zink, Robin
dc.contributor.author Lademann, Tobias
dc.date.accessioned 2025-03-28T09:44:37Z
dc.date.available 2025-03-28T09:44:37Z
dc.date.created 2025-03-24
dc.date.issued 2025-03-28
dc.description Here 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.identifier.uri https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4518
dc.rights.licenseCC-BY-4.0 (https://creativecommons.org/licenses/by/4.0)
dc.subject Load forecasting de_DE
dc.subject Thermal energy de_DE
dc.subject Machine Learning de_DE
dc.subject Industrial production systems de_DE
dc.subject.classification 4.22-01
dc.subject.ddc 620
dc.title Supplementary Material | An Empirical Comparison of Machine Learning Methods for Thermal Load Forecasting in Industrial Production Systems de_DE
dc.type Dataset de_DE
dcterms.accessRights openAccess
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
tuda.project Bund/BMBF | 03SFK3A0-3 | SynErgie3
tuda.unit TUDa

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