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

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).

Identifier
Source https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4518
Metadata Access https://tudatalib.ulb.tu-darmstadt.de/oai/openairedata?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:tudatalib.ulb.tu-darmstadt.de:tudatalib/4518
Provenance
Creator Zink, Robin; Lademann, Tobias
Publisher TU Darmstadt
Contributor TU Darmstadt
Publication Year 2025
Rights Creative Commons Attribution 4.0; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://tudatalib.ulb.tu-darmstadt.de/page/contact
Representation
Resource Type Dataset
Format text/csv
Discipline Other