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Article: Load profiling and its application to demand response: A review

TitleLoad profiling and its application to demand response: A review
Authors
KeywordsAdvanced Metering Infrastructure (AMI)
customer segmentation
data mining
demand response
load profiling
Issue Date2015
Citation
Tsinghua Science and Technology, 2015, v. 20, n. 2, p. 117-129 How to Cite?
AbstractThe smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentivebased, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.
Persistent Identifierhttp://hdl.handle.net/10722/308855
ISSN
2023 Impact Factor: 5.2
2023 SCImago Journal Rankings: 1.580
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Yi-
dc.contributor.authorChen, Qixin-
dc.contributor.authorKang, Chongqing-
dc.contributor.authorZhang, Mingming-
dc.contributor.authorWang, Ke-
dc.contributor.authorZhao, Yun-
dc.date.accessioned2021-12-08T07:50:16Z-
dc.date.available2021-12-08T07:50:16Z-
dc.date.issued2015-
dc.identifier.citationTsinghua Science and Technology, 2015, v. 20, n. 2, p. 117-129-
dc.identifier.issn1007-0214-
dc.identifier.urihttp://hdl.handle.net/10722/308855-
dc.description.abstractThe smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentivebased, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.-
dc.languageeng-
dc.relation.ispartofTsinghua Science and Technology-
dc.subjectAdvanced Metering Infrastructure (AMI)-
dc.subjectcustomer segmentation-
dc.subjectdata mining-
dc.subjectdemand response-
dc.subjectload profiling-
dc.titleLoad profiling and its application to demand response: A review-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1109/tst.2015.7085625-
dc.identifier.scopuseid_2-s2.0-84930677731-
dc.identifier.volume20-
dc.identifier.issue2-
dc.identifier.spage117-
dc.identifier.epage129-
dc.identifier.eissn1878-7606-
dc.identifier.isiWOS:000364490200001-

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