Rubiños, ManuelJove, EstebanGarcía-Ordás, María TeresaAbelha, AntonioAlaiz Moretón, Héctor2026-05-042026-05-042026-04-27Manuel Rubiños, Esteban Jove, María Teresa García-Ordás, Antonio Abelha, Héctor Alaiz-Moretón, Unsupervised AI-based water consumption classification from time series data, Logic Journal of the IGPL, Volume 34, Issue 3, June 2026, jzaf082, https://doi.org/10.1093/jigpal/jzaf0821368-9894https://hdl.handle.net/2183/48153Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Increasing concern about global water scarcity has highlighted the necessity to optimize the use of this resource, where AI and machine learning techniques can play a key role, serving as decision-making support tools that would help anticipate necessities and ensure a quality supply. This study proposes a methodology to characterize residential water users from daily consumption time series by applying feature extraction, dimensionality reduction and clustering techniques. Aggregated statistical features were first computed and then reduced via PCA. DBSCAN outperformed K-means in clustering, and both UMAP and t-SNE showed adequate embedded space results. Different characteristics were found between clusters, demonstrating the possibility of separating users of the same grid by behaviors or profiles.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Urban water managementUser characterizationClusteringTime series dataFeature extractionData explorationjournal articleopen access10.1093/jigpal/jzaf082