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Article: Clustering-based residential baseline estimation: A probabilistic perspective

TitleClustering-based residential baseline estimation: A probabilistic perspective
Authors
Keywordsclustering
Deep learning
demand response
dynamic time-of-use tariff
probabilistic baseline estimation
Issue Date2019
Citation
IEEE Transactions on Smart Grid, 2019, v. 10, n. 6, p. 6014-6028 How to Cite?
AbstractDemand response (DR) is one of the most cost-effective solutions for providing flexibility to power systems. The extensive deployment of DR trials and the roll-out of smart meters enable the quantification of consumer responsiveness to price signals via baseline estimation. The traditional deterministic baseline estimation approach can provide only a single value without consideration of uncertainty. This paper proposes a novel probabilistic baseline estimation framework that consists of a daily load profile pool construction stage, a deep learning-based clustering stage, an optimal cluster selection stage, and a quantile regression forests model construction stage. In particular, the concept of a daily load profile pool is introduced, and a deep-learning-based clustering approach is employed to handle a large number of daily patterns to further improve the baseline estimation performance. Case studies have been conducted on fine-grained smart meter data collected from a real dynamic time-of-use tariffs trial of the low carbon London project. The superior performance of the proposed method is demonstrated based on a series of evaluation metrics regarding both deterministic and probabilistic estimation results.
Persistent Identifierhttp://hdl.handle.net/10722/308795
ISSN
2023 Impact Factor: 8.6
2023 SCImago Journal Rankings: 4.863
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSun, Mingyang-
dc.contributor.authorWang, Yi-
dc.contributor.authorTeng, Fei-
dc.contributor.authorYe, Yujian-
dc.contributor.authorStrbac, Goran-
dc.contributor.authorKang, Chongqing-
dc.date.accessioned2021-12-08T07:50:09Z-
dc.date.available2021-12-08T07:50:09Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Smart Grid, 2019, v. 10, n. 6, p. 6014-6028-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/308795-
dc.description.abstractDemand response (DR) is one of the most cost-effective solutions for providing flexibility to power systems. The extensive deployment of DR trials and the roll-out of smart meters enable the quantification of consumer responsiveness to price signals via baseline estimation. The traditional deterministic baseline estimation approach can provide only a single value without consideration of uncertainty. This paper proposes a novel probabilistic baseline estimation framework that consists of a daily load profile pool construction stage, a deep learning-based clustering stage, an optimal cluster selection stage, and a quantile regression forests model construction stage. In particular, the concept of a daily load profile pool is introduced, and a deep-learning-based clustering approach is employed to handle a large number of daily patterns to further improve the baseline estimation performance. Case studies have been conducted on fine-grained smart meter data collected from a real dynamic time-of-use tariffs trial of the low carbon London project. The superior performance of the proposed method is demonstrated based on a series of evaluation metrics regarding both deterministic and probabilistic estimation results.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.subjectclustering-
dc.subjectDeep learning-
dc.subjectdemand response-
dc.subjectdynamic time-of-use tariff-
dc.subjectprobabilistic baseline estimation-
dc.titleClustering-based residential baseline estimation: A probabilistic perspective-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TSG.2019.2895333-
dc.identifier.scopuseid_2-s2.0-85073699519-
dc.identifier.volume10-
dc.identifier.issue6-
dc.identifier.spage6014-
dc.identifier.epage6028-
dc.identifier.eissn1949-3061-
dc.identifier.isiWOS:000507947800014-

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