Detail publikace

Efficient Integration of Machine Learning into District Heating Predictive Models

KUDELA, L. CHÝLEK, R. POSPÍŠIL, J.

Originální název

Efficient Integration of Machine Learning into District Heating Predictive Models

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

district heating; machine learning; optimization; modelling; dynamics; pipes; smart systems

Autoři

KUDELA, L.; CHÝLEK, R.; POSPÍŠIL, J.

Vydáno

2. 12. 2020

Nakladatel

MDPI

Místo

BASEL

ISSN

1996-1073

Periodikum

ENERGIES

Ročník

13

Číslo

23

Stát

Švýcarská konfederace

Strany od

1

Strany do

12

Strany počet

12

URL

https://www.mdpi.com/1996-1073/13/23/6381

Plný text v Digitální knihovně

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"
}