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Building-type classification based on measurements of energy consumption data
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-0835-7536
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-1624-5147
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-0139-0747
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0003-4589-7045
2014 (English)In: SMTDA 2014 Proceedings / [ed] H. Skiadas (Ed), ISAST: International Society for the Advancement of Science and Technology , 2014, p. 519-529Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we apply data-mining techniques to a classication problemon actual electricity consumption data from 350 Swedish households. Morespecically we use measurements of hourly electricity consumption during one monthand t classication models to the given data. The goal is to classify and later predict whether the building type of a specic household is an apartmentor a detached house. This classication/prediction problem becomes important ifone has a consumption time series for a household with unknown building type. Tocharacterise each household, we compute from the data some selected statistical attributesand also the load prole throughout the day for that household. The most important task here is to select a good representative set of feature variables, whichis solved by ranking the variable importance using technique of random forest. Wethen classify the data using classication tree method and linear discriminant analysis.The predictive power of the chosen classication models is plausible.

Place, publisher, year, edition, pages
ISAST: International Society for the Advancement of Science and Technology , 2014. p. 519-529
Keywords [en]
data-mining, energy consumption data, classication of energy customers, clustering of energy customers
National Category
Mathematics Building Technologies
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-26110ISBN: 978-618-81257-6-6 (print)OAI: oai:DiVA.org:mdh-26110DiVA, id: diva2:758211
Conference
3rd Stochastic Modelling Techniques and Data Analysis International Conference (SMTDA 2014), 11-14 June 2014, Lisbon, Portugal
Available from: 2014-10-24 Created: 2014-10-15 Last updated: 2017-10-03Bibliographically approved

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http://www.smtda.net/smtda2014.html

Authority records BETA

Ni, YingEngström, ChristopherMalyarenko, AnatoliyWallin, Fredrik

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CiteExportLink to record
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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