Big Data opportunities arise from high rate data streams acquired through smart sensors and smart meters, which, even for small water utilities, may produce a huge amount of data to be stored. This data enables the application of new data analytics to infer reliable predictive functionalities, with implications ranging from reducing No Revenue Water (NRW) to optimizing the water-energy nexus, meeting ever more pressing budgetary constraints. This paper presents the approach proposed in the EU-FP7-ICT project ICeWater, combining time series clustering, for the identification of typical daily urban water demand patterns, and Support Vector Regression for performing a short term forecast. Promising results obtained on the Water Distribution Network (WDN) in Milan are presented. The approach has been designed to also be applied on smart metering data related to individual customers, addressing Big Data analytics issues. © 2014 WIT Press.

Candelieri, A., Archetti, F. (2014). Smart water in urban distribution networks: Limited financial capacity and Big Data analytics. In Water and Society 2015 (pp. 63-73). WITPress [10.2495/UW140061].

Smart water in urban distribution networks: Limited financial capacity and Big Data analytics

CANDELIERI, ANTONIO
Primo
;
ARCHETTI, FRANCESCO ANTONIO
Ultimo
2014

Abstract

Big Data opportunities arise from high rate data streams acquired through smart sensors and smart meters, which, even for small water utilities, may produce a huge amount of data to be stored. This data enables the application of new data analytics to infer reliable predictive functionalities, with implications ranging from reducing No Revenue Water (NRW) to optimizing the water-energy nexus, meeting ever more pressing budgetary constraints. This paper presents the approach proposed in the EU-FP7-ICT project ICeWater, combining time series clustering, for the identification of typical daily urban water demand patterns, and Support Vector Regression for performing a short term forecast. Promising results obtained on the Water Distribution Network (WDN) in Milan are presented. The approach has been designed to also be applied on smart metering data related to individual customers, addressing Big Data analytics issues. © 2014 WIT Press.
Capitolo o saggio
Predictive analytics; Short-term demand forecasting; Smart water management; Architecture Environmental Science (all); Building and Construction; Safety, Risk, Reliability and Quality; Civil and Structural Engineering; Pattern Recognition; Arts and Humanities (miscellaneous)
English
Water and Society 2015
2014
9781845647803
139
WITPress
63
73
Candelieri, A., Archetti, F. (2014). Smart water in urban distribution networks: Limited financial capacity and Big Data analytics. In Water and Society 2015 (pp. 63-73). WITPress [10.2495/UW140061].
reserved
File in questo prodotto:
File Dimensione Formato  
Smart water in urban distribution networks_Urban Water II vol 139_UW14006FU1.pdf

Solo gestori archivio

Dimensione 1.19 MB
Formato Adobe PDF
1.19 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/59701
Citazioni
  • Scopus 11
  • ???jsp.display-item.citation.isi??? ND
Social impact