Datum
2024-05-20Schlagwort
333 Boden- und Energiewirtschaft 620 Ingenieurwissenschaften PrognoseMaschinelles LernenInkrementelles LernenFernwärmeversorgungWärmespeicherungLastspitzenkappungMetadata
Zur Langanzeige
Aufsatz
Peak shaving at system level with a large district heating substation using deep learning forecasting models
Zusammenfassung
The decarbonisation of urban district heating (DH) systems requires increased heating grid flexibility. Therefore, this article examines the optimised operation of a tank thermal energy storage (TTES) on the secondary side of a new DH substation for an industrial site in a German city, in order to shave the peaks of the whole DH system and thus reduce the need for heat-only boilers (HOB). The accuracy of heat load and return temperature forecasts for both the industrial consumer and the DH grid is critical to the performance of the optimisation-based operating strategy of the TTES. Therefore, long short-term memory neural networks are used in combination with continuous model updates through incremental learning to create two forecasting scenarios, one using only preceding data for the forecasts and the other including future weather data. The results show that high forecasting accuracy is most relevant for reducing the annual maximum peak, with a reduction of 2.8% in the preceding data scenario, 4% with future weather data and 7% in a benchmark with perfect forecasts. The economic viability of the storage through HOB heat savings is primarily affected by lower forecasting accuracy when the additional cost of HOB heat is less than 60 €/MWh.
Zitierform
In: Energy Volume 301 (2024-05-20) eissn:1873-6785Förderhinweis
Gefördert im Rahmen des Projekts DEALZitieren
@article{doi:10.17170/kobra-2024060610287,
author={Trabert, Ulrich and Pag, Felix and Orozaliev, Janybek and Jordan, Ulrike and Vajen, Klaus},
title={Peak shaving at system level with a large district heating substation using deep learning forecasting models},
journal={Energy},
year={2024}
}
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2024-06-10T11:04:46Z 2024-06-10T11:04:46Z 2024-05-20 doi:10.17170/kobra-2024060610287 http://hdl.handle.net/123456789/15827 Gefördert im Rahmen des Projekts DEAL eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ forecasting machine learning incremental learning large district heating substation thermal storage peak shaving 333 620 Peak shaving at system level with a large district heating substation using deep learning forecasting models Aufsatz The decarbonisation of urban district heating (DH) systems requires increased heating grid flexibility. Therefore, this article examines the optimised operation of a tank thermal energy storage (TTES) on the secondary side of a new DH substation for an industrial site in a German city, in order to shave the peaks of the whole DH system and thus reduce the need for heat-only boilers (HOB). The accuracy of heat load and return temperature forecasts for both the industrial consumer and the DH grid is critical to the performance of the optimisation-based operating strategy of the TTES. Therefore, long short-term memory neural networks are used in combination with continuous model updates through incremental learning to create two forecasting scenarios, one using only preceding data for the forecasts and the other including future weather data. The results show that high forecasting accuracy is most relevant for reducing the annual maximum peak, with a reduction of 2.8% in the preceding data scenario, 4% with future weather data and 7% in a benchmark with perfect forecasts. The economic viability of the storage through HOB heat savings is primarily affected by lower forecasting accuracy when the additional cost of HOB heat is less than 60 €/MWh. open access Trabert, Ulrich Pag, Felix Orozaliev, Janybek Jordan, Ulrike Vajen, Klaus doi:10.1016/j.energy.2024.131690 Prognose Maschinelles Lernen Inkrementelles Lernen Fernwärmeversorgung Wärmespeicherung Lastspitzenkappung publishedVersion eissn:1873-6785 Energy Volume 301 false 131690
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