Publication
Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona case study
Conference Article
Conference
IFAC World Congress (IFAC)
Edition
19th
Pages
10457-10462
Doc link
http://dx.doi.org/10.3182/20140824-6-ZA-1003.01343
File
Authors
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Sampathirao, Ajay Kumar
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Grosso Pérez, Juan Manuel
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Sopasakis, Pantelis
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Ocampo Martínez, Carlos A.
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Bemporad, Alberto
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Puig Cayuela, Vicenç
Projects associated
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.
Categories
automation, control theory, optimisation, predictive control.
Author keywords
disturbance forecasting, large-scale systems. model predictive control, industrial processes
Scientific reference
A.K. Sampathirao, J.M. Grosso, P. Sopasakis, C. Ocampo-Martínez, A. Bemporad and V. Puig. Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona case study, 19th IFAC World Congress, 2014, Cape Town, South Africa, pp. 10457-10462.
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