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Multi-Method Approach to Compare the Socio-Demographic Typology of Residents and Clusters of Electricity Load Curves in a Swiss Sustainable Neighbourhood

In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016

Author

Listed:
  • Cimmino, Francesco
  • Mastelic, Joelle
  • Genoud, Stephane

Abstract

A sustainable neighbourhood was built Switzerland by one of the leaders in this field. Half of the 400 apartments have been equipped with smart meters delivering big data on energy consumption (electricity, water, heating…). The company would like to know if it is possible to link socio-demographic typology of residents with energy consumption patterns. To answer this question we present in this article a multimethod approach combining qualitative analysis, frequently used in marketing (multiple correspondence analyses), and quantitative analysis from applied statistics to answer this question. First, we have conducted a survey among the residents of the sustainable neighbourhood to gather socio-demographic data, and then we have proposed a marketing typology of residents. In parallel, we have analysed load curves with statistical models (clustering factors, hermano beta models, coincidence factors, som, expert practice) to see if there are patterns of energy consumption and to determine groups of similar load curves. Then we have compared the discrepancies in the composition of the groups between both methods. This study is based on a single case study generating a new research hypothesis: the typology of residents based on socio-demographic data can be linked to energy consumption pattern of a household.

Suggested Citation

  • Cimmino, Francesco & Mastelic, Joelle & Genoud, Stephane, 2016. "Multi-Method Approach to Compare the Socio-Demographic Typology of Residents and Clusters of Electricity Load Curves in a Swiss Sustainable Neighbourhood," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2016), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016, pages 310-314, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    • Handle: RePEc:zbw:entr16:183731
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    References listed on IDEAS

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    1. Wehrens, Ron & Buydens, Lutgarde M. C., 2007. "Self- and Super-organizing Maps in R: The kohonen Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i05).
    2. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
    3. Julie Josse & Marie Chavent & Benot Liquet & François Husson, 2012. "Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis," Journal of Classification, Springer;The Classification Society, vol. 29(1), pages 91-116, April.
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    More about this item

    Keywords

    applied statistics; typology; energy; cluster; sustainable consumption; research;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

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