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Load Forecasting in District Heating Systems Using Stacked Ensembles of Machine Learning Algorithms

  • For district heating, heat demand forecasting is playing a key role for an optimised power plant dispatch. Machine Learning can help to significantly improve forecasts of thermal loads. The prediction quality of neural networks is higher than that of decision trees in most cases. However, compared to decision trees neural networks have weaknesses when extrapolating outside known ranges. This work presents a novel method called “Deep DHC” (Deep Learning for District Heating and Cooling), which combines these two approaches in order to benefit from strengths of both methods. On the one hand, the novel approach uses conventional decision tree based regression algorithms such as the AdaBoost and Random Forest, as well as artificial neural networks. In addition to common feed forward neural networks (FNN), a deep learning network structure, which consists of long short-term memory (LSTM) cells, is used for the first time. The LSTM method has already proven to be very powerful in modern speech recognition. In order to achieve best possibleFor district heating, heat demand forecasting is playing a key role for an optimised power plant dispatch. Machine Learning can help to significantly improve forecasts of thermal loads. The prediction quality of neural networks is higher than that of decision trees in most cases. However, compared to decision trees neural networks have weaknesses when extrapolating outside known ranges. This work presents a novel method called “Deep DHC” (Deep Learning for District Heating and Cooling), which combines these two approaches in order to benefit from strengths of both methods. On the one hand, the novel approach uses conventional decision tree based regression algorithms such as the AdaBoost and Random Forest, as well as artificial neural networks. In addition to common feed forward neural networks (FNN), a deep learning network structure, which consists of long short-term memory (LSTM) cells, is used for the first time. The LSTM method has already proven to be very powerful in modern speech recognition. In order to achieve best possible heat demand forecasts, the aforementioned methods for load forecasting are combined and weighted by an additional machine learning method. Results show that it is possible to achieve a further improvement in forecasting quality for district heating loads by purposefully combining individual forecasting methods. Hence, mean and absolute deviations are significantly reduced in comparison to the individual methods.show moreshow less

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Metadaten
Author:Till Faber, Matthias FinkenrathORCiDGND
DOI:https://doi.org/10.2991/ahe.k.210202.001
Identifier:978-94-6239-327-1 OPAC HS OPAC extern
Identifier:2589-4943 OPAC HS OPAC extern
Parent Title (English):Proceedings of the 14th International Renewable Energy Storage Conference 2020 (IRES 2020)
Publisher:Atlantis Press
Place of publication:Dordrecht, Paris
Document Type:conference proceeding (article)
Conference:14th International Renewable Energy Storage Conference 2020 (IRES 2020), 10.-12. März 2020, Düsseldorf, Germany
Language:English
Date of Publication (online):2021/02/04
Year of first Publication:2021
Tag:Deep Learning; KI; KWK-Flex; Lastprognose; Maschinelles Lernen
GND Keyword:KWK-Flex; Lastprognose; KI; Maschinelles Lernen; Deep Learning; deepDHC
Number of pages:4 Seiten
First Page:1
Last Page:4
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
Research Project:KWK-Flex – Hochflexible stromgeführte Kraft-Wärme-Kopplung durch thermische Speicher und "Power-to-Heat"-Technologien
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International
Release Date:2021/03/17
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