Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis

Decarbonization scenarios advocate the transformation of energy systems to a decentralized grid of prosumers. However, in heterogeneous energy systems, profiling of end-users is still to be investigated. As a matter of fact, the knowledge of electrical load dynamics is instrumental to the system eff...

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Autores principales:	Portera Rosario, Bonacina Fabrizio, Corsini Alessandro, Miele Eric Stefan, Ricciardi Celsi Lorenzo
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/ade162896eab4f39b7c4f2ca047e4ea3
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spelling	oai:doaj.org-article:ade162896eab4f39b7c4f2ca047e4ea32021-11-08T15:18:51ZEnergy profiling of end-users in service and industry sectors with use of Complex Network Analysis2267-124210.1051/e3sconf/202131210001https://doaj.org/article/ade162896eab4f39b7c4f2ca047e4ea32021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/88/e3sconf_ati2021_10001.pdfhttps://doaj.org/toc/2267-1242Decarbonization scenarios advocate the transformation of energy systems to a decentralized grid of prosumers. However, in heterogeneous energy systems, profiling of end-users is still to be investigated. As a matter of fact, the knowledge of electrical load dynamics is instrumental to the system efficiency and the optimization of energy dispatch strategies. Recently, a number of clustering algorithms have been proposed to group load diagrams with similar shapes, generating typical profiles. To this end, conventional clustering algorithms are unable to capture the temporal dynamics and sequential relationships among data. This circumstance is of paramount importance in the service and industrial sectors where energy consumption trends over time are possibly non-stationary. In this paper, we aim to reconstruct the annual user energy profile identified through a non-conventional method which combines a time series clustering algorithm, namely K-Means with Dynamic Time Warping, with Complex Network Analysis. For the purpose of the present research, we have used an open database containing the data of 100 commercial and industrial consumers, collected every 5 minutes over a year. From the results, it is possible to identify different patterns of consumer behaviour and similar corporate profiles without any prior knowledge of the raw data.Portera RosarioBonacina FabrizioCorsini AlessandroMiele Eric StefanRicciardi Celsi LorenzoEDP SciencesarticleEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 312, p 10001 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Environmental sciences GE1-350
spellingShingle	Environmental sciences GE1-350 Portera Rosario Bonacina Fabrizio Corsini Alessandro Miele Eric Stefan Ricciardi Celsi Lorenzo Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis
description	Decarbonization scenarios advocate the transformation of energy systems to a decentralized grid of prosumers. However, in heterogeneous energy systems, profiling of end-users is still to be investigated. As a matter of fact, the knowledge of electrical load dynamics is instrumental to the system efficiency and the optimization of energy dispatch strategies. Recently, a number of clustering algorithms have been proposed to group load diagrams with similar shapes, generating typical profiles. To this end, conventional clustering algorithms are unable to capture the temporal dynamics and sequential relationships among data. This circumstance is of paramount importance in the service and industrial sectors where energy consumption trends over time are possibly non-stationary. In this paper, we aim to reconstruct the annual user energy profile identified through a non-conventional method which combines a time series clustering algorithm, namely K-Means with Dynamic Time Warping, with Complex Network Analysis. For the purpose of the present research, we have used an open database containing the data of 100 commercial and industrial consumers, collected every 5 minutes over a year. From the results, it is possible to identify different patterns of consumer behaviour and similar corporate profiles without any prior knowledge of the raw data.
format	article
author	Portera Rosario Bonacina Fabrizio Corsini Alessandro Miele Eric Stefan Ricciardi Celsi Lorenzo
author_facet	Portera Rosario Bonacina Fabrizio Corsini Alessandro Miele Eric Stefan Ricciardi Celsi Lorenzo
author_sort	Portera Rosario
title	Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis
title_short	Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis
title_full	Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis
title_fullStr	Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis
title_full_unstemmed	Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis
title_sort	energy profiling of end-users in service and industry sectors with use of complex network analysis
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/ade162896eab4f39b7c4f2ca047e4ea3
work_keys_str_mv	AT porterarosario energyprofilingofendusersinserviceandindustrysectorswithuseofcomplexnetworkanalysis AT bonacinafabrizio energyprofilingofendusersinserviceandindustrysectorswithuseofcomplexnetworkanalysis AT corsinialessandro energyprofilingofendusersinserviceandindustrysectorswithuseofcomplexnetworkanalysis AT mieleericstefan energyprofilingofendusersinserviceandindustrysectorswithuseofcomplexnetworkanalysis AT ricciardicelsilorenzo energyprofilingofendusersinserviceandindustrysectorswithuseofcomplexnetworkanalysis
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Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis

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