Rubiños, ManuelÁlvarez-Crespo, Marta MaríaZayas-Gato, FranciscoCalvo-Rolle, José Luis2025-01-202025-01-202024http://hdl.handle.net/2183/40799This work presents the first steps of the integration of AI techniques in the field of urban water management, more precisely in the line of urban consumption forecasting and user characterization. For this purpose, different data visualization and clustering techniques are applied and the results are compared, in order to study which ones are more suitable for pattern extraction and detection. The resulting user profiles are then contrasted to determine common characteristics among the same group members and differences among others. Average daily consumption and variability are proposed as the variables on which the characterization is based.engAtribución 4.0http://creativecommons.org/licenses/by/4.0/Data scienceData visualizationUser characterizationWater consumptionUrban water managementArtificial IntelligenceClusteringconference outputopen access