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Article: Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications

TitleClustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications
Authors
Keywordsbehavior dynamics
big data
demand response
distributed clustering
electricity consumption
Load profiling
Markov model
Issue Date2016
Citation
IEEE Transactions on Smart Grid, 2016, v. 7, n. 5, p. 2437-2447 How to Cite?
AbstractIn a competitive retail market, large volumes of smart meter data provide opportunities for load serving entities to enhance their knowledge of customers' electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics, where 'dynamics' refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods. First, for each individual customer, symbolic aggregate approximation is performed to reduce the scale of the data set, and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of load curves to several state transition matrixes. Second, a clustering technique by fast search and find of density peaks (CFSFDP) is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two consumption patterns measured by the Kullback-Liebler distance, and to classify the customers into several clusters. To tackle the challenges of big data, the CFSFDP technique is integrated into a divide-And-conquer approach toward big data applications. A numerical case verifies the effectiveness of the proposed models and approaches.
Persistent Identifierhttp://hdl.handle.net/10722/308919
ISSN
2023 Impact Factor: 8.6
2023 SCImago Journal Rankings: 4.863
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Yi-
dc.contributor.authorChen, Qixin-
dc.contributor.authorKang, Chongqing-
dc.contributor.authorXia, Qing-
dc.date.accessioned2021-12-08T07:50:24Z-
dc.date.available2021-12-08T07:50:24Z-
dc.date.issued2016-
dc.identifier.citationIEEE Transactions on Smart Grid, 2016, v. 7, n. 5, p. 2437-2447-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/308919-
dc.description.abstractIn a competitive retail market, large volumes of smart meter data provide opportunities for load serving entities to enhance their knowledge of customers' electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics, where 'dynamics' refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods. First, for each individual customer, symbolic aggregate approximation is performed to reduce the scale of the data set, and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of load curves to several state transition matrixes. Second, a clustering technique by fast search and find of density peaks (CFSFDP) is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two consumption patterns measured by the Kullback-Liebler distance, and to classify the customers into several clusters. To tackle the challenges of big data, the CFSFDP technique is integrated into a divide-And-conquer approach toward big data applications. A numerical case verifies the effectiveness of the proposed models and approaches.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.subjectbehavior dynamics-
dc.subjectbig data-
dc.subjectdemand response-
dc.subjectdistributed clustering-
dc.subjectelectricity consumption-
dc.subjectLoad profiling-
dc.subjectMarkov model-
dc.titleClustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TSG.2016.2548565-
dc.identifier.scopuseid_2-s2.0-84984604132-
dc.identifier.volume7-
dc.identifier.issue5-
dc.identifier.spage2437-
dc.identifier.epage2447-
dc.identifier.isiWOS:000391722100027-

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