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Smart meters time series clustering for demand response applications in the context of high penetration of renewable energy resources

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dc.contributor.authorBañales López, Santiago
dc.contributor.authorDormido Canto, Raquel
dc.contributor.authorDuro Carralero, Natividad
dc.date.accessioned2025-10-01T07:16:15Z
dc.date.available2025-10-01T07:16:15Z
dc.date.issued2021-06-11
dc.descriptionThe registered version of this article, first published in Energies, 14(12). 3458, is available online at the publisher's website: https://doi.org/10.3390/en14123458
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Energies, 14(12). 3458, está disponible en línea en el sitio web del editor: https://doi.org/10.3390/en14123458
dc.descriptionThis work was supported in part by the Spanish Ministry of Economy and Competitiveness under the Project CICYT RTI2018-094665-B-I00, the Project PID2019-108377RB-C32 and the Project DPI2017-84259-C2-2-R. In addition, the Project GID2016-6 supported by UNED.
dc.description.abstractThe variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one. In parallel, the massive introduction of smart meters allows for the use of high frequency energy use time series data to segment electricity customers according to their demand response potential. This paper proposes a smart meter time series clustering methodology based on a two-stage k-medoids clustering of normalized load-shape time series organized around the day divided into 48 time points. Time complexity is drastically reduced by first applying the k-medoids on each customer separately, and second on the total set of customer representatives. Further time complexity reduction is achieved using time series representation with low computational needs. Customer segmentation is undertaken with only four easy-to-interpret features: average energy use, energy–temperature correlation, entropy of the load-shape representative vector, and distance to wind generation patterns. This last feature is computed using the dynamic time warping distance between load and expected wind generation shape representative medoids. The two-stage clustering proves to be computationally effective, scalable and performant according to both internal validity metrics, based on average silhouette, and external validation, based on the ground truth embedded in customer surveys.en
dc.description.versionversión publicada
dc.identifier.citationBañales, S., Dormido, R., & Duro, N. (2021). Smart meters time series clustering for demand response applications in the context of high penetration of renewable energy resources. Energies, 14(12). 3458 (22 pp). https://doi.org/10.3390/EN14123458
dc.identifier.doihttps://doi.org/10.3390/en14123458
dc.identifier.issn1996-1073
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30293
dc.journal.issue12
dc.journal.titleEnergies
dc.journal.volume14
dc.language.isoen
dc.publisherMDPI
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInformática y Automática
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108377RB-C32/ES/MODELADO DE TIPOS DE DISRUPCIONES EN PLASMAS TERMONUCLEARES Y SU RECONOCIMIENTO MEDIANTE TECNICAS DE APRENDIZAJE AUTOMATICO
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject3304 Tecnología de los ordenadores
dc.subject.keywordstime series clusteringen
dc.subject.keywordstime series representationen
dc.subject.keywordselectrical smart metersen
dc.subject.keywordsdemand responseen
dc.subject.keywordsrenewable energyen
dc.subject.keywordsclustering validationen
dc.titleSmart meters time series clustering for demand response applications in the context of high penetration of renewable energy resourcesen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationd8964856-5d49-4779-87df-331494bd4336
relation.isAuthorOfPublicationd5087903-00fc-427e-b4cf-f0592d122b30
relation.isAuthorOfPublication.latestForDiscoveryd8964856-5d49-4779-87df-331494bd4336
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