Optimal Node Grouping for Water Distribution System Demand Estimation

Jung, Donghwi; Choi, Young Hwan; Kim, Joong Hoon

doi:10.3390/w8040160

Cited by 18 publications

(4 citation statements)

References 35 publications

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“…The problem of sensor placement has been addressed in literature by considering budget constraints and the quality of measured data [2], location impact metrics [3,4], and the time taken to detect contamination events [5,6]. The number of sensor locations may also be reduced by clustering water demand nodes into groups [7], grouping nodes according to water quality characteristics [8], and using reduced network models [9]. A ranking technique is used to select nodes for locating pressure sensors in [10].…”

Section: Introductionmentioning

confidence: 99%

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

Letting

Hamam

Abu‐Mahfouz

2017

Water

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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.

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Section: Introductionmentioning

confidence: 99%

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

Letting

Hamam

Abu‐Mahfouz

2017

Water

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“…This is done by a patient-level data analysis that allows to develop a different predictive model per DMA. The approach is naturally completed taking into account the existing correlations between DMAs of the same WDS [62,63]. EDAAs provide, in this way, a spatial sense of analysis to the common temporal approach when approaching water demand modelling at urban level.…”

Section: Epidemiology-based Data Analysis For Water Demandmentioning

confidence: 99%

Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

Navarrete-López

Herrera

Brentan

et al. 2019

Water

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Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. The proposal has been tested with databases from a Brazilian water utility, providing key knowledge for improving water management and hydraulic operation of the distribution system. This novel analysis framework shows several benefits in terms of accuracy and performance of neural network models for water demand.

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“…In this method, nodal water demands with similar characteristics are grouped as one parameter to be estimated. However, the nonlinear relationship between the model parameters and outputs is not considered, and improper grouping may result in additional errors (Jung et al 2016). The SVD method has been used as an efficient method to estimate the nodal water demand.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Nodal Water Demand Estimation in Large-scale Water Distribution Systems

Chu

Zhang

Zhou

et al. 2022

Water Resour Manage

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Real-time modeling of the water distribution system (WDS) is a critical step for the control and operation of such systems. The nodal water demand as the most important time-varying parameter must be estimated in real-time. The computational burden of nodal water demand estimation is intensive, leading to inefficiency for the modeling of the large-scale network. The Jacobian matrix computation and Hessian matrix inversion are the processes that dominate the main computation time. To address this problem, an approach to shorten the computational time for the real-time demand estimation in the large-scale network is proposed. The approach can efficiently compute the Jacobian matrix based on solving a system of linear equations, and a Hessian matrix inversion method based on matrix partition and Iterative Woodbury-Matrix-Identity Formula is proposed. The developed approach is applied to a large-scale network, of which the number of nodal water demand is 12523, and the number of measurements ranging from 10 to 2000.Results show that the time consumptions of both Jacobian computation and Hessian matrix inversion are significantly shortened compared with the existing approach.

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Optimal Node Grouping for Water Distribution System Demand Estimation

Cited by 18 publications

References 35 publications

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

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

Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

An Efficient Approach for Nodal Water Demand Estimation in Large-scale Water Distribution Systems

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