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Clustering and classification of building structures and their construction years with respect to monthly electricity consumption: An Initial Case Study of Tunabyggen AB Borlänge
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In urban planning and city energy management, building structure and construction years may differ, which often leads to different electricity consumption. Systematic approaches for classifying buildings according to their energy performance may help urban management make sustainable decisions. The aim of this research is to study monthly electricity consumption of different buildings located at different areas of Tunabyggen AB in Borlänge municipality and extract those buildings structures that have similar features in terms of monthly electricity. This research also studied different categories of building structures and their construction years.   Monthly electricity data is collected in kWh of the year 2018 from Tunabyggen AB in Borlänge. Based on the electricity consumption patterns, dimension reduction is used for implementing clustering analysis due to the feature of the data. Buildings consuming highest and lowest electricity were extracted. Analysis is made on different categories of building structures.   The results show that a multi-family house built in 1950s and 1970s are the most representative building structure that consume lowest electricity.  On the other hand, apartment buildings from 1905s to 1970s are the most common buildings and could indicate high electricity consumption. However, the cluster with highest electricity consumption does not have too much observations and each individual feature needs to be examined in the future.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Dimension reduction, cluster analysis, classification, building electricity consumption
National Category
Other Social Sciences
Identifiers
URN: urn:nbn:se:du-32333OAI: oai:DiVA.org:du-32333DiVA, id: diva2:1415592
Available from: 2020-03-19 Created: 2020-03-19

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf