2026-04-13T04:49:19Zhttps://recercat.cat/oai/request
oai:recercat.cat:2117/264732025-07-17T10:23:46Zcom_2072_1033col_2072_452950
RECERCAT author García Valverde, Diego author González, d author Quevedo Casín, Joseba Jokin author Puig Cayuela, Vicenç author Saludes Closa, Jordi 2015 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 ReviewedPostprint (author’s final draft) Open Access Àrees temàtiques de la UPC::Informàtica::Automàtica i control Smart meters water demand clustering big data Aigua -- Abastament -- Control Mesurament de consum d'aigua Water demand estimation and outlier detection from smart meter data using classification and Big Data methods Conference report