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A review of data-driven approaches for prediction and classification of building energy consumption
Dalarna University, School of Technology and Business Studies, Energy Technology.ORCID iD: 0000-0002-2369-0169
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2018 (English)In: Renewable and Sustainable Energy Reviews, ISSN 1364-0321, Vol. 82, 1027-1047 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 82, 1027-1047 p.
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Energy Engineering
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Energy, Forests and Built Environments
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URN: urn:nbn:se:du-26385DOI: 10.1016/j.rser.2017.09.108OAI: oai:DiVA.org:du-26385DiVA: diva2:1147985
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2017-10-11Bibliographically approved

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Zhang, XingxingHan, MengjieZhao, Xiaoyun
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
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