Abstract
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.
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