Gaussian-process-based demand forecasting for predictive control of drinking water networks
Document typeConference report
Rights accessOpen Access
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.
CitationWang, Y., Ocampo-Martinez, C.A., Puig, V., Quevedo, J. Gaussian-process-based demand forecasting for predictive control of drinking water networks. A: International Conference on Critical Information Infrastructure Security. "9th International Conference on Critical Information Infrastructure Security". Limassol: 2014, p. 1-12.