### Abstract

Real-time state estimation is defined as the process of calculating the state variable of interest in real time not being directly measured. In a water distribution system (WDS), nodal demands are often considered as the state variable (i.e., unknown variable) and can be estimated using nodal pressures and pipe flow rates measured at sensors installed throughout the system. Nodes are often grouped for aggregation to decrease the number of unknowns (demands) in the WDS demand estimation problem. This study proposes an optimal node grouping model to maximize the real-time WDS demand estimation accuracy. This Kalman filter-based demand estimation method is linked with a genetic algorithm for node group optimization. The modified Austin network demand is estimated to demonstrate the proposed model. True demands and field measurements are synthetically generated using a hydraulic model of the study network. Accordingly, the optimal node groups identified by the proposed model reduce the total root-mean-square error of the estimated node group demand by 24% compared to that determined by engineering knowledge. Based on the results, more pipe flow sensors should be installed to measure small flows and to further enhance the demand estimation accuracy.

Original language | English |
---|---|

Article number | 160 |

Journal | Water (Switzerland) |

Volume | 8 |

Issue number | 4 |

DOIs | |

Publication status | Published - 2016 |

### Fingerprint

### Keywords

- Demand estimation
- Genetic algorithm
- Kalman filter
- Node grouping
- Water distribution system

### ASJC Scopus subject areas

- Aquatic Science
- Biochemistry
- Water Science and Technology
- Geography, Planning and Development

### Cite this

*Water (Switzerland)*,

*8*(4), [160]. https://doi.org/10.3390/w8040160

**Optimal node grouping for water distribution system demand estimation.** / Jung, Donghwi; Choi, Young Hwan; Kim, Joong Hoon.

Research output: Contribution to journal › Article

*Water (Switzerland)*, vol. 8, no. 4, 160. https://doi.org/10.3390/w8040160

}

TY - JOUR

T1 - Optimal node grouping for water distribution system demand estimation

AU - Jung, Donghwi

AU - Choi, Young Hwan

AU - Kim, Joong Hoon

PY - 2016

Y1 - 2016

N2 - Real-time state estimation is defined as the process of calculating the state variable of interest in real time not being directly measured. In a water distribution system (WDS), nodal demands are often considered as the state variable (i.e., unknown variable) and can be estimated using nodal pressures and pipe flow rates measured at sensors installed throughout the system. Nodes are often grouped for aggregation to decrease the number of unknowns (demands) in the WDS demand estimation problem. This study proposes an optimal node grouping model to maximize the real-time WDS demand estimation accuracy. This Kalman filter-based demand estimation method is linked with a genetic algorithm for node group optimization. The modified Austin network demand is estimated to demonstrate the proposed model. True demands and field measurements are synthetically generated using a hydraulic model of the study network. Accordingly, the optimal node groups identified by the proposed model reduce the total root-mean-square error of the estimated node group demand by 24% compared to that determined by engineering knowledge. Based on the results, more pipe flow sensors should be installed to measure small flows and to further enhance the demand estimation accuracy.

AB - Real-time state estimation is defined as the process of calculating the state variable of interest in real time not being directly measured. In a water distribution system (WDS), nodal demands are often considered as the state variable (i.e., unknown variable) and can be estimated using nodal pressures and pipe flow rates measured at sensors installed throughout the system. Nodes are often grouped for aggregation to decrease the number of unknowns (demands) in the WDS demand estimation problem. This study proposes an optimal node grouping model to maximize the real-time WDS demand estimation accuracy. This Kalman filter-based demand estimation method is linked with a genetic algorithm for node group optimization. The modified Austin network demand is estimated to demonstrate the proposed model. True demands and field measurements are synthetically generated using a hydraulic model of the study network. Accordingly, the optimal node groups identified by the proposed model reduce the total root-mean-square error of the estimated node group demand by 24% compared to that determined by engineering knowledge. Based on the results, more pipe flow sensors should be installed to measure small flows and to further enhance the demand estimation accuracy.

KW - Demand estimation

KW - Genetic algorithm

KW - Kalman filter

KW - Node grouping

KW - Water distribution system

UR - http://www.scopus.com/inward/record.url?scp=84965151258&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84965151258&partnerID=8YFLogxK

U2 - 10.3390/w8040160

DO - 10.3390/w8040160

M3 - Article

AN - SCOPUS:84965151258

VL - 8

JO - Water (Switzerland)

JF - Water (Switzerland)

SN - 2073-4441

IS - 4

M1 - 160

ER -