Supplementary Material | An Empirical Comparison of Machine Learning Methods for Thermal Load Forecasting in Industrial Production Systems
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Beschreibung
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).
DFG-Fächer
4.22-01 EnergieverfahrenstechnikZugehörige Drittmittelprojekte
Bund/BMBF | 03SFK3A0-3 | SynErgie3Sammlungen
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