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oai:recercat.cat:2117/264732025-07-17T10:23:46Zcom_2072_1033col_2072_452950

Water demand estimation and outlier detection from smart meter data using classification and Big Data methods García Valverde, Diego González, d Quevedo Casín, Joseba Jokin Puig Cayuela, Vicenç Saludes Closa, Jordi Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada II Universitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control Àrees temàtiques de la UPC::Informàtica::Automàtica i control Smart meters water demand clustering big data Aigua -- Abastament -- Control Mesurament de consum d'aigua Automatic Meter Reading (AMR) systems are being deployed in many cities to obtain insight into the status and the behavior of District Metering Area (DMA) with more granularity. Until now, the water consumption readings of the population were taken one per month or one each two-months. In contrast, AMR systems provide hourly readings for households and more frequent readings for big consumers. On the one hand, this paper aims at predicting water demand and detect suspicious behaviors – e.g. a leak, a smart meter break down or even a fraud – by extracting water consumption patterns. On the other hand, the main contribution of this paper, a software framework, based on Big Data techniques, is presented to tackle the barriers of traditional data storage and data analysis since the volume of AMR data collected by Water Utilities is enormous and it is continuously growing because this technology is expanding . Peer Reviewed Postprint (author’s final draft) 2015 Conference report Garcia, D. [et al.]. Water demand estimation and outlier detection from smart meter data using classification and Big Data methods. A: New Developments in IT & Water. "2nd New Developments in IT & Water Conference, 8-10 February 2015, Rotterdam (Holland)". Rotterdam: 2015, p. 1-8. https://hdl.handle.net/2117/26473 eng Open Access 8 p. application/pdf