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Smart meter data clustering using consumption indicators: responsibility factor and consumption variability
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0003-4589-7045
2017 (English)In: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY / [ed] Yan, J Wu, J Li, H, ELSEVIER SCIENCE BV , 2017, p. 2236-2242Conference paper, Published paper (Refereed)
Abstract [en]

The wide spread of smart metering roll out enables a better understanding of the consumer behavior and tailoring demand response DR programs to achieve cost-efficient energy savings. In the residential sector smart metering allows detailed readings of the power consumption in the form of large volumes time series that encodes relevant information for distribution network operators DNOs to manage in optimal ways low-voltage networks. Further, DNOs may leverage the smart meter data to identify customer group for energy efficiency programs and demand side response DSR (e.g., dynamic pricing schemes). In this paper, we outline the application of smart meter data mining to identify consumers who are more responsible for the peak system using responsibility factor and consumption variability. Identification of consumers having higher responsibility to the peak system may yield to better enhance energy reduction recommendations and enable more tailored dynamic pricing plans depending on the consumer's influence on the utility peak. Responsibility factor and consumption variance have been investigated as input features of the clustering algorithms. Two clustering techniques, hierarchical clustering and self-organising map SOM, have been used to study the resulting customer groups and to have an effective graphical visualization of the customer's cluster distribution on the input feature space.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. p. 2236-2242
Series
Energy Procedia, ISSN 1876-6102 ; 142
Keywords [en]
Demand response, Smart metering, Consumers clustering, Responsibility factor, Variabiliy
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-42256DOI: 10.1016/j.egypro.2017.12.624ISI: 000452901602061Scopus ID: 2-s2.0-85041517474OAI: oai:DiVA.org:mdh-42256DiVA, id: diva2:1274905
Conference
9th International Conference on Applied Energy (ICAE), AUG 21-24, 2017, Cardiff, ENGLAND
Available from: 2019-01-03 Created: 2019-01-03 Last updated: 2019-01-16Bibliographically approved

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Maher, AzazaWallin, 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
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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