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.