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.

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

Gabriele L.
;
Bertacchini F.;Giglio S.;Menniti D.;Pantano P.;Pinnarelli A.;Sorrentino N.;Bilotta E.
2020

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.
978-3-030-39080-8
978-3-030-39081-5
Cluster analysis
Consumer behaviour
Machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/310011
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