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KUDELA, L. CHÝLEK, R. POSPÍŠIL, J.
Original Title
Efficient Integration of Machine Learning into District Heating Predictive Models
Type
journal article in Web of Science
Language
English
Original Abstract
Modern control strategies for district-level heating and cooling supply systems pose a difficult challenge. In order to integrate a wide range of hot and cold sources, these new systems will rely heavily on accumulation and much lower operating temperatures. This means that predictive models advising the control strategy must take into account long-lasting thermal effects but must not be computationally too expensive, because the control would not be possible in practice. This paper presents a simple but powerful systematic approach to reducing the complexity of individual components of such models. It makes it possible to combine human engineering intuition with machine learning and arrive at comprehensive and accurate models. As an example, a simple steady-state heat loss of buried pipes is extended with dynamics observed in a much more complex model. The results show that the process converges quickly toward reasonable solutions. The new auto-generated model performs 5 x 10(4) times faster than its complex equivalent while preserving essentially the same accuracy. This approach has great potential to enhance the development of fast predictive models not just for district heating. Only open-source software was used, while OpenModelica, Python, and FEniCS were predominantly used.
Keywords
district heating; machine learning; optimization; modelling; dynamics; pipes; smart systems
Authors
KUDELA, L.; CHÝLEK, R.; POSPÍŠIL, J.
Released
2. 12. 2020
Publisher
MDPI
Location
BASEL
ISBN
1996-1073
Periodical
ENERGIES
Year of study
13
Number
23
State
Swiss Confederation
Pages from
1
Pages to
12
Pages count
URL
https://www.mdpi.com/1996-1073/13/23/6381
Full text in the Digital Library
http://hdl.handle.net/11012/195829
BibTex
@article{BUT166351, author="Libor {Kudela} and Radomír {Chýlek} and Jiří {Pospíšil}", title="Efficient Integration of Machine Learning into District Heating Predictive Models", journal="ENERGIES", year="2020", volume="13", number="23", pages="1--12", doi="10.3390/en13236381", issn="1996-1073", url="https://www.mdpi.com/1996-1073/13/23/6381" }