This paper presents a computational framework performing, in two stages: urban water demand pattern characterization through time series clustering and reliable hourly water demand forecasting for the entire day based on Support Vector Machine (SVM) regression. An SVM regression model is trained for each cluster identified and for each hour of the day, taking the hourly water demand data acquired at the very first m hours of the day. The approach has been validated on a real case study that is the urban water demand of the Water Distribution Network (WDN) in Milan, managed by Metropolitana Milanese, one of the partner of the EU-FP7-ICT ICeWater project

Candelieri, A., Archetti, F. (2014). Identifying Typical Urban Water Demand Patterns for a Reliable Short-term Forecasting – The Icewater Project Approach. PROCEDIA ENGINEERING, 89, 1004-1012 [10.1016/j.proeng.2014.11.218].

Identifying Typical Urban Water Demand Patterns for a Reliable Short-term Forecasting – The Icewater Project Approach

Candelieri, A;Archetti, F
2014

Abstract

This paper presents a computational framework performing, in two stages: urban water demand pattern characterization through time series clustering and reliable hourly water demand forecasting for the entire day based on Support Vector Machine (SVM) regression. An SVM regression model is trained for each cluster identified and for each hour of the day, taking the hourly water demand data acquired at the very first m hours of the day. The approach has been validated on a real case study that is the urban water demand of the Water Distribution Network (WDN) in Milan, managed by Metropolitana Milanese, one of the partner of the EU-FP7-ICT ICeWater project
Articolo in rivista - Articolo scientifico
urban water demand; forecasting; chategorization; pattern; time series clustering; support vector regression
English
2014
89
1004
1012
open
Candelieri, A., Archetti, F. (2014). Identifying Typical Urban Water Demand Patterns for a Reliable Short-term Forecasting – The Icewater Project Approach. PROCEDIA ENGINEERING, 89, 1004-1012 [10.1016/j.proeng.2014.11.218].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/59678
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