On-line hydraulic state prediction for water distribution systems

Abstract

This paper describes and demonstrates a method for on‐line hydraulic state prediction in urban water networks. The proposed method uses a Predictor‐Corrector (PC) approach in which a statistical data‐driven algorithm is applied to estimate future water demands, while near real‐time field measurements are used to correct (i.e., calibrate) these predicted values on‐line. The calibration problem is solved using a modified Least Squares (LS) fit method. The objective function is the minimization of the least‐squares of the differences between predicted and measured hydraulic parameters (i.e., pressure and flow rates at several system locations), with the decision variables being the consumers' water demands. The a‐priori estimation (i.e., prediction) of the values of the decision variables, which improves through experience, facilitates a better convergence of the calibration model and provides adequate information on the system's hydraulic state for real time optimization. The proposed methodology is demonstrated on a prototypical municipal water distribution system.