Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization

Abstract

Demand estimation in a water distribution network provides crucial data for monitoring and controlling systems. Because of budgetary and physical constraints, there is a need to estimate water demand from a limited number of sensor measurements. The demand estimation problem is underdetermined because of the limited sensor data and the implicit relationships between nodal demands and pressure heads. A simulation optimization technique using the water distribution network hydraulic model and an evolutionary algorithm is a potential solution to the demand estimation problem. This paper presents a detailed process simulation model for water demand estimation using the particle swarm optimization (PSO) algorithm. Nodal water demands and pipe flows are estimated when the number of estimated parameters is more than the number of measured values. The water demand at each node is determined by using the PSO algorithm to identify a corresponding demand multiplier. The demand multipliers are encoded with varying step sizes and the optimization algorithm particles are also discretized in order to improve the computation time. The sensitivity of the estimated water demand to uncertainty in demand multiplier discrete values and uncertainty in measured parameters is investigated. The sensor placement locations are selected using an analysis of the sensitivity of measured nodal heads and pipe flows to the change in the water demand. The results show that nodal demands and pipe flows can be accurately determined from a limited number of sensors.

Memes Evolution in a Memetic Variant of Particle Swarm Optimization

“…Particle swarm optimization (PSO), originally introduced in [18], is a meta-heuristic approach to continuous optimization problems that is inspired by the collective behavior observed in flocks of birds. Further variants of PSO have been proposed in [12,[27][28][29][30][31][32][33].…”

Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems

“…This issue contains 18 papers which focus on some of the mentioned problems of water distribution system management. The key points are: (i) design of water system [1][2][3][4]; (ii) optimization of network performance assessment [5][6][7][8]; (iii) monitoring and diagnosis of pressure pipe system [9][10][11]; (iv) optimal water quality management [12][13][14]; and (v) modelling and forecasting of water demand [15][16][17][18].…”

“…Four papers of this issue examine the last key point, modelling and forecasting of water demand. Firstly, Letting et al [15] present a water demand calibration approach. The approach is aimed at estimating the water demand multiplier at each node of a water distribution system model by minimizing the error between observed and simulated nodal head and pipe flow rates.…”

jHawanet: An Open-Source Project for the Implementation and Assessment of Multi-Objective Evolutionary Algorithms on Water Distribution Networks

“…Metaheuristic methods such as multi-objective evolutionary algorithms (MOEAs) have been successfully used to optimize WDNs [1,2]. The versatility of these algorithms has allowed their use for problems related to water resources such as leak detection [3,4], optimal pipe sizing [5,6], water quality [7], water demand [8], drainage networks design [9], irrigation networks [10,11], etc. The success of such algorithms lies in their capacity to explore the space of feasible solutions and generate an approximation to the set of solutions in a single execution.…”