A data-driven methodology for deriving electricity consumption typologies from smart meters

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dc.contributor.authorQuesada Granja, Carlos
dc.contributor.authorMontero Manso, Pablo
dc.contributor.authorPflugradt, Noah
dc.contributor.authorAstigarraga, Leire
dc.contributor.authorMerveille, Chris
dc.contributor.authorCasado Mansilla, Diego
dc.contributor.authorBorges Hernández, Cruz E.
dc.date.accessioned2025-10-14T13:47:07Z
dc.date.available2025-10-14T13:47:07Z
dc.date.issued2025-09-22
dc.date.updated2025-10-14T13:47:07Z
dc.description.abstractWe present a data-driven methodology to identify residential electricity consumption typologies from large-scale smart meter data. The proposed approach combines seasonal feature extraction, clustering via Self-Organizing Maps (SOMs), and expert-in-the-loop validation to ensure both statistical robustness and operational relevance. Although the methodology is tailored to residential consumption, it also captures other non-residential load patterns – such as commercial, industrial, and public-sector profiles – present in the analyzed data. The methodology was applied to more than 23,000 time series from five international datasets, resulting in 40 distinct consumption patterns. These clusters were grouped into five behavioral categories – primary residences, holiday homes, equipment-intensive households, offices, and public lighting – each capturing characteristic daily and seasonal load signatures. The resulting typology enables a more realistic and interpretable representation of household electricity use, improving the design of demand-side strategies, tariff schemes, and forecasting models. In particular, it offers a practical foundation for energy planning and policy targeting across diverse regions, and can inform real-time classification tools or adaptive services under conditions of data scarcity or external disruptions.en
dc.description.sponsorshipThe research leading to this study received funding from the European Union Horizon 2020 research and innovation pro-gram under Grant Agreement No. 891943 (Climbing the Causal-ity Ladder to Understand and Project the Energy Demand of the Residential Sector, WHY project), the Basque Government through the ELKARTEK program, Spain (KK-2023/00083, AI4EDER project), and the Basque Government, Spain grant Grupos de investigación del Sistema Universitario Vasco, Departamento de Educación, Universidades e Investigación (Research group: IT1677-22)en
dc.identifier.citationQuesada, C., Montero-Manso, P., Pflugradt, N., Astigarraga, L., Merveille, C., Casado-Mansilla, D., & Borges, C. E. (2025). A data-driven methodology for deriving electricity consumption typologies from smart meters. Energy Reports, 14, 2420-2434. https://doi.org/10.1016/J.EGYR.2025.09.002
dc.identifier.doi10.1016/J.EGYR.2025.09.002
dc.identifier.eissn2352-4847
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3963
dc.language.isoeng
dc.publisherElsevier Ltd
dc.rights© 2025 The Authors
dc.subject.otherConsumption typology
dc.subject.otherDemand-side management
dc.subject.otherEnergy behavior
dc.subject.otherFlexible energy systems
dc.subject.otherLoad profile clustering
dc.subject.otherResidential electricity consumption
dc.subject.otherSelf-organizing maps (SOM)
dc.subject.otherSmart meter data
dc.subject.otherTime series features
dc.subject.otherUnsupervised learning
dc.titleA data-driven methodology for deriving electricity consumption typologies from smart metersen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage2434
oaire.citation.startPage2420
oaire.citation.titleEnergy Reports
oaire.citation.volume14
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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