Peak shaving at system level with a large district heating substation using deep learning forecasting models
| dc.date.accessioned | 2024-06-10T11:04:46Z | |
| dc.date.available | 2024-06-10T11:04:46Z | |
| dc.date.issued | 2024-05-20 | |
| dc.description.sponsorship | Gefördert im Rahmen des Projekts DEAL | ger |
| dc.identifier | doi:10.17170/kobra-2024060610287 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/15827 | |
| dc.language.iso | eng | |
| dc.relation.doi | doi:10.1016/j.energy.2024.131690 | |
| dc.rights | Namensnennung 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | forecasting | eng |
| dc.subject | machine learning | eng |
| dc.subject | incremental learning | eng |
| dc.subject | large district heating substation | eng |
| dc.subject | thermal storage | eng |
| dc.subject | peak shaving | eng |
| dc.subject.ddc | 333 | |
| dc.subject.ddc | 620 | |
| dc.subject.swd | Prognose | ger |
| dc.subject.swd | Maschinelles Lernen | ger |
| dc.subject.swd | Inkrementelles Lernen | ger |
| dc.subject.swd | Fernwärmeversorgung | ger |
| dc.subject.swd | Wärmespeicherung | ger |
| dc.subject.swd | Lastspitzenkappung | ger |
| dc.title | Peak shaving at system level with a large district heating substation using deep learning forecasting models | eng |
| dc.type | Aufsatz | |
| dc.type.version | publishedVersion | |
| dcterms.abstract | 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. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Trabert, Ulrich | |
| dcterms.creator | Pag, Felix | |
| dcterms.creator | Orozaliev, Janybek | |
| dcterms.creator | Jordan, Ulrike | |
| dcterms.creator | Vajen, Klaus | |
| dcterms.source.articlenumber | 131690 | |
| dcterms.source.identifier | eissn:1873-6785 | |
| dcterms.source.journal | Energy | eng |
| dcterms.source.volume | Volume 301 | |
| kup.iskup | false |