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Investigating the added values of high frequency energy consumption data using data mining techniques
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (Mathematics/Applied Mathematics)ORCID iD: 0000-0002-0835-7536
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.ORCID iD: 0000-0002-1624-5147
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.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: AIP Conference Proceedings 1637 (2014): Volume number: 1637; Published: 10 december 2014 / [ed] Seenith Sivasundaram, AIP Publishing , 2014, 734-743 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we apply data-mining techniques to customer classification and clustering tasks on actual electricity consumption data from 350 Swedish households. For the classification task we classify households into different categories based on some statistical attributes of their energy consumption measurements. For the clustering task, we use average daily load diagrams to partition electricity-consuming households into distinct groups. The data contains electricity consumption measurements on each 10-minute time interval for each light source and electrical appliance. We perform the classification and clustering tasks using four variants of processeddata sets corresponding to the 10-minute total electricity consumption aggregated from all electrical sources, the hourly total consumption aggregated over all 10-minute intervals during that clock hour, the total consumption over each four-hour intervals and finally the daily total consumption. The goal is to see if there are any differences in using data sets of various frequency levels. We present the comparison results and investigate the added value of the high-frequency measurements, for example 10-minute measurements, in terms of its influence on customer clustering and classification.

Place, publisher, year, edition, pages
AIP Publishing , 2014. 734-743 p.
National Category
Computer and Information Science
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-26917DOI: 10.1063/1.4904645ISI: 000347812200088ISBN: 978-0-7354-1276-7 (print)OAI: oai:DiVA.org:mdh-26917DiVA: diva2:771975
Conference
10TH INTERNATIONAL CONFERENCE ON MATHEMATICAL PROBLEMS IN ENGINEERING, AEROSPACE AND SCIENCES: ICNPAA 2014 Conference date: 15–18 July 2014 Location: Narvik, Norway
Available from: 2014-12-15 Created: 2014-12-15 Last updated: 2017-01-09Bibliographically approved

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Publisher's full texthttp://dx.doi.org/10.1063/1.4904645

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CiteExportLink to record
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Citation style
  • apa
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