%0 DATA
%A Nhu, Cuong
%A Angus R., Simpson
%A Jochen W., Deurelein
%A Olivier, Piller
%D 2017
%T CCWI2017: F10 'Online Demand Estimation of Geographical and Non-Geographical Distributed Demand Pattern in Water Distribution Networks'
%U https://figshare.shef.ac.uk/articles/journal_contribution/CCWI2017_F10_Online_Demand_Estimation_of_Geographical_and_Non-Geographical_Distributed_Demand_Pattern_in_Water_Distribution_Networks_/5364133
%R 10.15131/shef.data.5364133.v1
%2 https://figshare.shef.ac.uk/ndownloader/files/9218686
%K CCWi2017
%K Particle filtering
%K real time demand estimation
%K water distribution systems,
%K calibration
%X The issue of demand calibration and estimation under uncertainty is known to be an exceptionally difficult problem in water distribution system modelling. In the context of real-time event modelling, the stochastic behaviour of the water demands and non-geographical distribution of the demand patterns makes it even more complicated. This paper considers a predictor – corrector approach, implemented by a particle filter model, for solving the problem of demand multiplier factor estimation. A demand forecasting model is used to predict the water demand multiplier factors. The EPANET hydraulic solver is applied to simulate the hydraulic behaviour of a water network. Real time observations are integrated via a formulation of the particle filter model to correct the demand predictions. A water distribution network of realistic size with two configurations of demand patterns (geographically distributed demand patterns and non-geographically distributed demand patterns) are used to evaluate the particle filter model. Results show that the model is able to provide good estimation of the demand multiplier factors in a near real-time context if the measurement errors are small. Large measurement errors may result in inaccurate estimates of the demand values.