@unpublished{eprints2326,
       booktitle = {Proceedings of the 19th IFAC World Congress},
          author = {Ajay Kumar Sampathirao and Juan Manuel Grosso P{\'e}rez and Pantelis Sopasakis and Carlos Ocampo-Martinez and Alberto Bemporad and Vicen{\c c} Puig},
       publisher = {IFAC},
            note = {19th IFAC World Congress, August 24-29, 2014, Cape Town, South Africa},
            year = {2014},
           title = {Water demand forecasting for the optimal operation of large-scale drinking water networks: the Barcelona case study},
           pages = {10457--10462},
           month = {August},
        keywords = {Water supply and distribution systems; Control of large-scale systems; Model predictive and optimization-based control; Process control applications},
             url = {http://eprints.imtlucca.it/2326/},
        abstract = {Drinking Water Networks (DWN) are large-scale multiple-input multiple-output systems with uncertain disturbances (such as the water demand from the consumers) and involve components of linear, non-linear and switching nature. Operating, safety and quality constraints deem it important for the state and the input of such systems to be constrained into a given domain. Moreover, DWNs' operation is driven by time-varying demands and involves an considerable consumption of electric energy and the exploitation of limited water resources. Hence, the management of these networks must be carried out optimally with respect to the use of available resources and infrastructure, whilst satisfying high service levels for the drinking water supply. To accomplish this task, this paper explores various methods for demand forecasting, such as Seasonal ARIMA, BATS and Support Vector Machine, and presents a set of statistically validated time series models. These models, integrated with a Model Predictive Control (MPC) strategy addressed in this paper, allow to account for an accurate on-line forecasting and flow management of a DWN.}
}