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A model-based smart meters time series decomposition approach for demand flexibility characterization of SMEs and households

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dc.contributor.authorBaƱales, Santiago
dc.contributor.authorDormido Canto, Raquel
dc.contributor.authorDuro Carralero, Natividad
dc.date.accessioned2026-03-13T13:44:10Z
dc.date.available2026-03-13T13:44:10Z
dc.date.issued2026-03-10
dc.descriptionThe registered version of this article, first published in ā€œEnergy Reports 15 (2026), 109153", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.egyr.2026.109153
dc.descriptionLa versiĆ³n registrada de este artĆ­culo, publicado por primera vez en ā€œEnergy Reports 15 (2026), 109153", estĆ” disponible en lĆ­nea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.egyr.2026.109153
dc.description.abstractDemand flexibility is a fundamental service in the current energy system transformation contributing to balance intermittent renewable generation with increasing electrified demand. This paper proposes an innovative model-based methodology for smart meters time series decomposition to characterize and segment customers for demand flexibility programs. First, the daily energy times are split into normalized daily and hourly profiles, and periods of structurally low energy use are identified. The series is then decomposed using a dynamic regression and ARIMA-GARCH approach, resulting in residuals that follow a non-Gaussian white noise distribution. Next, complexity reduction for normalized hourly energy use is achieved via a regression-based feature engineering model, which feeds into a k-means clustering procedure to determine similar hourly energy profile patterns for working week and weekend days. The decomposition yields a cohesive set of data-driven, baseline-free, and explainable metrics that quantify and characterize the demand flexibility potential of each customer and cluster. These metrics capture key dimensions such as flexibility quantity, variability, reversion speed, calendar effects, and predictability. The methodology is validated on a real publicly available dataset of Irish households and SMEs customers. The results highlight the robustness and replicability of the approach while providing actionable insights and comparison between the two customer segments. This approach enables energy companies, engaged citizens and other stakeholders to design and deploy effective demand flexibility strategies in the energy industry.en
dc.description.provenanceMade available in DSpace on 2026-03-13T13:44:10Z (GMT). No. of bitstreams: 1 DuroCarralero_Natividad_DemandFlexibility_NATIVIDAD DURO CARRA.pdf: 9420750 bytes, checksum: 2214f993e986bda7fd68c3b98fe32435 (MD5) Previous issue date: 2026-03-10en
dc.description.versionversiĆ³n publicada
dc.identifier.citationBaƱales, Santiago, Dormido, Raquel, Duro, Natividad, (2026). A model-based smart meters time series decomposition approach for demand flexibility characterization of SMEs and households. Energy Reports 15, 109153. https://doi.org/10.1016/j.egyr.2026.109153
dc.identifier.doihttps://doi.org/10.1016/j.egyr.2026.109153
dc.identifier.eissn2352-4847
dc.identifier.urihttps://hdl.handle.net/20.500.14468/32117
dc.journal.titleEnergy Reports
dc.journal.volume15
dc.language.isoen
dc.page.final15
dc.page.initial1
dc.publisherElsevier
dc.relation.centerEscuela TƩcnica Superior de Ingenierƭa InformƔtica
dc.relation.departmentInformƔtica y AutomƔtica
dc.relation.researchgroupIngenierĆ­a de Sistemas y Control (ISCO)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject1203 Ciencia de los ordenadores
dc.subject.keywordsSmart meters analyticsen
dc.subject.keywordsTimes series clusteringen
dc.subject.keywordsDemand flexibilityen
dc.subject.keywordsDemand responseen
dc.titleA model-based smart meters time series decomposition approach for demand flexibility characterization of SMEs and householdsen
dc.typeartĆ­culoes
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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