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Título:	Gaussian-process-based demand forecasting for predictive control of drinking water networks
Autor/a:	Wang, Ye; Ocampo-Martínez, Carlos; Puig Cayuela, Vicenç; Quevedo Casín, Joseba Jokin
Otros autores:	Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
Abstract:	This paper focuses on water demand forecasting for predictive control of Drinking Water Networks (DWN) in the short term by using Gaussian Process (GP). For the predictive control strategy, system states in a finite horizon are generated by a DWN model and demands are regarded as system disturbances. The goal is to provide a demand estimation within a given confidence interval. For the sake of obtaining a desired forecasting performance, the forecasting process is carried out in two parts: the expected part is forecasted by Double-Seasonal Holt-Winters (DSHW) method and the stochastic part is forecasted by GP method. The mean value of water demand is firstly estimated by DSHW while GP provides estimations within a confidence interval. GP is applied with random inputs to propagate uncertainty at each step. Results of the application of the proposed approach to a real case study based on the Barcelona DWN have shown that the general goal has been successfully reached.
Abstract:	Peer Reviewed
Materia(s):	-Àrees temàtiques de la UPC::Informàtica::Automàtica i control -Gaussian Process -Water demand forecasting -Drinking water networks -Double-seasonal Holt-Winters -Predictive Control -Classificació INSPEC::Automation
Derechos:	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Tipo de documento:	Artículo - Versión presentada Objeto de conferencia
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