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Benchmarking of state-of-the-art machine learning methods for highly accurate thermal load forecasting in district heating networks

  • Decarbonisation of heat generation has become a priority for district heating network operators. In order to avoid the use of fossil-fired boilers, operators need to know peaks in heat demand in advance. Accurate thermal load forecasting is playing an increasingly important role in this respect. This paper presents the final results of the research project “deepDHC” (deep learning for district heating and cooling) funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). The three-year project focused on systematically benchmarking thermal load forecasts for district heating networks, based on state-of-the-art machine learning methods. The analysis covers a variety of machine learning techniques, such as neural networks – including latest deep learning methods – (e.g. LSTM, TFT, ESN, RC), decision trees (random forests, adaptive boosting, XGB) and statistical methods (SARIMAX). In addition, the impact of combining methods by so-called “stacking” was investigated. Training and validation of the machineDecarbonisation of heat generation has become a priority for district heating network operators. In order to avoid the use of fossil-fired boilers, operators need to know peaks in heat demand in advance. Accurate thermal load forecasting is playing an increasingly important role in this respect. This paper presents the final results of the research project “deepDHC” (deep learning for district heating and cooling) funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). The three-year project focused on systematically benchmarking thermal load forecasts for district heating networks, based on state-of-the-art machine learning methods. The analysis covers a variety of machine learning techniques, such as neural networks – including latest deep learning methods – (e.g. LSTM, TFT, ESN, RC), decision trees (random forests, adaptive boosting, XGB) and statistical methods (SARIMAX). In addition, the impact of combining methods by so-called “stacking” was investigated. Training and validation of the machine learning algorithms was based on historical operating data from the district heating network for the city of Ulm in Germany, in combination with historical weather data, and weather forecasts. Thermal load forecasts – typically for three days ahead – are presented and compared against one another. An automatic tuning routine was developed as part of the project, which enables regular re-training of the machine learning algorithms based on the latest operating data from the heating network. Furthermore, a web interface for real-time forecasting was developed and implemented at the power station.show moreshow less

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Metadaten
Author:Christian Pressa, Stefan LeiprechtORCiD, Fabian Behrens, Verena Jetzinger, Hendric Popma, Matthias FinkenrathORCiDGND
URL / DOI:https://www.proceedings.com/content/069/069564-0283open.pdf
DOI:https://doi.org/10.52202/069564-0283
Identifier:9781713874928 OPAC HS OPAC extern
Parent Title (English):36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2023)
Publisher:Curran Associates, Inc.
Place of publication:New York
Document Type:conference proceeding (article)
Conference:ECOS 2023, 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, 25-30 June 2023, Las Palmas de Gran Canaria, Spain
Language:English
Date of Publication (online):2023/08/09
Year of first Publication:2023
Tag:Fernwärme; Lastprognose; Maschinelles Lernen; deepDHC
GND Keyword:Maschinelles Lernen
Volume:2023
Number of pages:9 Seiten
First Page:3154
Last Page:3162
Institutes:Fakultät Maschinenbau
IEAT - Institut für Energie- und Antriebstechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Open Access:open_access
Research focus:FSP1: Energie
Publication Lists:Finkenrath, Matthias
Behrens, Fabian
Leiprecht, Stefan
Pressa, Christian
Jetzinger, Verena
Publication reviewed:begutachtet
Release Date:2023/10/10
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