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dc.contributor.authorSampathirao, Ajay Kumar
dc.contributor.authorGrosso Pérez, Juan Manuel
dc.contributor.authorSopasakis, Pantelis
dc.contributor.authorOcampo-Martínez, Carlos
dc.contributor.authorBemporad, Alberto
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2015-07-31T12:32:02Z
dc.date.available2015-07-31T12:32:02Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationSampathirao, A., Grosso, J.M., Sopasakis, P., Ocampo-Martinez, C.A., Bemporad, A., Puig, V. Water demand forecasting for the optimal operation of large-scale drinking water networks: the Barcelona case study. A: World Congress of the International Federation of Automatic Control. "Proceedings of the 19th IFAC World Congress, 2014". Cape Town: International Federation of Automatic Control (IFAC), 2014, p. 10457-10462.
dc.identifier.isbn978-3-902823-62-5
dc.identifier.urihttp://hdl.handle.net/2117/76478
dc.description.abstractDrinking 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.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherInternational Federation of Automatic Control (IFAC)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.otherautomation
dc.subject.othercontrol theory
dc.subject.otheroptimisation
dc.subject.otherpredictive control
dc.subject.otherdisturbance forecasting
dc.subject.otherlarge-scale systems. model predictive control
dc.subject.otherindustrial processes
dc.titleWater demand forecasting for the optimal operation of large-scale drinking water networks: the Barcelona case study
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.3182/20140824-6-ZA-1003.01343
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Optimisation
dc.rights.accessOpen Access
local.identifier.drac15271290
dc.description.versionPostprint (author’s final draft)
local.citation.authorSampathirao, A.; Grosso, J.M.; Sopasakis, P.; Ocampo-Martinez, C.A.; Bemporad, A.; Puig, V.
local.citation.contributorWorld Congress of the International Federation of Automatic Control
local.citation.pubplaceCape Town
local.citation.publicationNameProceedings of the 19th IFAC World Congress, 2014
local.citation.startingPage10457
local.citation.endingPage10462


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