Publication
Gaussian-process-based demand forecasting for predictive control of drinking water networks
Conference Article
Conference
International Conference on Critical Information Infrastructures Security (CRITIS)
Edition
9th
Pages
69-80
Doc link
http://dx.oi.org/10.1007/978-3-319-31664-2_8
File
Authors
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Wang, Ye
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Ocampo Martínez, Carlos A.
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Puig Cayuela, Vicenç
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Quevedo Casín, Joseba-Jokin
Projects associated
Abstract
This paper focuses on short-term water demand forecasting for predictive control of DrinkingWater Networks (DWN) by using Gaussian Process (GP). For the predictive control strategy, system state prediction in a nite 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 condence 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 rstly estimated by DSHW while GP provides estimations within a condence 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.
Categories
automation, observability, predictive control.
Author keywords
Gaussian process, water demand forecasting, drinking water networks, double-seasonal holt-winters, predictive control
Scientific reference
Y. Wang, C. Ocampo-Martínez, V. Puig and J. Quevedo. Gaussian-process-based demand forecasting for predictive control of drinking water networks, 9th International Conference on Critical Information Infrastructures Security, 2014, Limassol, Cyprus, in Critical Information Infrastructures Security, Vol 8985 of Lecture Notes in Computer Science, pp. 69-80, 2016, Springer.
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