Application of reinforcement learning for energy consumption optimization of district heating system

Jifei Deng*, Miro Eklund, Seppo Sierla, Jouni Savolainen, Hannu Niemistö, Tommi Karhela, Valeriy Vyatkin

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

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Abstract

Heating residential spaces consumed 64 percent of total household energy consumption in Finland. Considering the heat transfer and time delay in the district heating system, the calculation of setpoints of supply temperature requires a comprehensive understanding of the real system, and experienced operators need to manually determine the setpoints. To save energy, a more effective and accurate method is needed for setpoints calculation. In this paper, a reinforcement learning based method is proposed. Through interacting with an Apros-based simulation model, the agents learn to calculate supply temperature parallelly for lowering energy costs. Simulation results show that the proposed method outperforms the existing method and has the potential to address the problem in real factories.

Original languageEnglish
Title of host publication2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)
PublisherIEEE
ISBN (Electronic)979-8-3503-9971-4
ISBN (Print)979-8-3503-9972-1
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Article in a conference publication
EventIEEE International Symposium on Industrial Electronics -
Duration: 19 Jun 202321 Jun 2023

Publication series

Name Proceedings of the IEEE International Symposium on Industrial Electronics
ISSN (Print)2163-5137

Conference

ConferenceIEEE International Symposium on Industrial Electronics
Abbreviated titleISIE
Period19/06/2321/06/23

Keywords

  • district heating
  • energy consumption optimization
  • reinforcement learning

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