Gaussian-Process-Based Demand Forecasting for Predictive Control of Drinking Water Networks

  • Ye WangEmail author
  • Carlos Ocampo-Martínez
  • Vicenç Puig
  • Joseba Quevedo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8985)


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.


Gaussian process Water demand forecasting Drinking Water Networks Double-Seasonal Holt-Winters Predictive control 



This work is partially supported by the research projects CICYT SHERECS DPI-2011-26243 and ECOCIS DPI-2013-48243-C2-1-R, both of the Spanish Ministry of Education, by EFFINET grant FP7-ICT-2012-318556 of the European Commission and by AGAUR Doctorat Industrial 2013-DI-041. Ye Wang also thanks China Scholarship Council for providing postgraduate scholarship.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ye Wang
    • 1
    Email author
  • Carlos Ocampo-Martínez
    • 1
  • Vicenç Puig
    • 1
  • Joseba Quevedo
    • 1
  1. 1.Automatic Control DepartmentTechnical University of CataloniaTerrassaSpain

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