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An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (EST)
2016 (English)In: The 8th International Conference on Applied Energy – ICAE2016, 2016Conference paper, Abstract (Refereed)
Place, publisher, year, edition, pages
2016.
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:mdh:diva-34708OAI: oai:DiVA.org:mdh-34708DiVA: diva2:1068136
Conference
The 8th International Conference on Applied Energy – ICAE2016
Available from: 2017-01-24 Created: 2017-01-24 Last updated: 2017-01-24Bibliographically approved

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Maher, Azaza
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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