Publication detail

Efficient Integration of Machine Learning into District Heating Predictive Models

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

12

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"
}