Gaussian-process-based demand forecasting for predictive control of drinking water networks

Trabajo presentado a la 9th International Conference on Critical Information Infrastructures Security, celebrada en Limassol (Chipre) del 13 al 15 de octubre de 2014.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.This work is partially supported by the research projects 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.Peer Reviewe