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Clustering Analysis to Profile Customers’ Behaviour in POWER CLOUD Energy Community

  • Lorella GabrieleEmail author
  • Francesca Bertacchini
  • Simona Giglio
  • Daniele Menniti
  • Pietro Pantano
  • Anna Pinnarelli
  • Nicola Sorrentino
  • Eleonora Bilotta
Conference paper
  • 46 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11973)

Abstract

This paper presents a cluster analysis study on energy consumption dataset to profile “groups of customers” to whom address POWERCLOUD services. POWER CLOUD project (PON I& C2014–2020) aims to create an energy community where each consumer can become also energy producer (PROSUMER) and so exchange a surplus of energy produced by renewable sources with other users, or collectively purchase or sell wholesale energy. In this framework, an online questionnaire has been developed in order to collect data on consumers behaviour and their preferences. A clustering analysis was carried on the filled questionnaires using Wolfram Mathematica software, in particular FindClusters function, to automatically group related segments of data. In our work, clustering analysis allowed to better understand the energy consumption propensity according the identified demographic variables. Thus, the outcomes highlight how the availability to adopt technologies to be used in PowerCloud energy community, increases with the growth of the family unit and, a greater propensity is major present in the age groups of 18–24 and 25–34.

Keywords

Machine learning Cluster analysis Consumer behaviour 

Notes

Acknowledgements

This research was supported by the following grants POWERCLOUD (PON I&C2014-2020-MISE F/050159/01-03/X32).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of CalabriaArcavacata di RendeItaly

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