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A machine learning approach to increase energy efficiency in district heating systems
Gjøvik University College, Norway.ORCID iD: 0000-0001-7520-695x
Gjøvik University College, Norway.
Faculty of Technology and Management, Norway.
2015 (English)In: Environmental Engineering and Computer Application: Proceedings of the International Conference on Environmental Engineering and Computer Application, ICEECA 2014, Hong kong: CRC Press, 2015, 223-226 p.Conference paper, Published paper (Refereed)
Abstract [en]

Heat demand prediction is an important part of increasing system efficiency within district heating. To achieve this efficiency, the energy provider companies need to estimate how much energy is re quired to satisfy the market demand. In this paper, we propose a method to investigate the application of online ma chine learning algorithm to achieve energy efficiency and optimization in District Heating (DH) systems by predicting the heat demand on the consumer side. To accomplish this, we are planning to use operational data from a Norwegian company (EffektivEnergi AS, Hamar) for a group of buildings that are connected to DH in other places.

Place, publisher, year, edition, pages
Hong kong: CRC Press, 2015. 223-226 p.
National Category
Computer Science
Research subject
Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-67840DOI: 10.1201/b18565-44ISBN: 978-1-138-02807-4 (print)ISBN: 978-1-315-68538-0 (electronic)OAI: oai:DiVA.org:lnu-67840DiVA: diva2:1139235
Conference
International Conference on Environmental Engineering and Computer Application (ICEECA 2014, Hong Kong, 25-26 December, 2014
Available from: 2017-09-07 Created: 2017-09-07 Last updated: 2017-09-15Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf