Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization
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 becau...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/9/8/593 |
| _version_ | 1811268281355468800 |
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| author | Lawrence K. Letting Yskandar Hamam Adnan M. Abu-Mahfouz |
| author_facet | Lawrence K. Letting Yskandar Hamam Adnan M. Abu-Mahfouz |
| author_sort | Lawrence K. Letting |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T21:20:09Z |
| format | Article |
| id | doaj.art-32ccbbbc2f454eddbd3f90179c6ed600 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-12T21:20:09Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-32ccbbbc2f454eddbd3f90179c6ed6002022-12-22T03:16:20ZengMDPI AGWater2073-44412017-08-019859310.3390/w9080593w9080593Estimation of Water Demand in Water Distribution Systems Using Particle Swarm OptimizationLawrence K. Letting0Yskandar Hamam1Adnan M. Abu-Mahfouz2Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaDepartment of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaDepartment of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaDemand 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.https://www.mdpi.com/2073-4441/9/8/593water demand estimationdemand multipliersunderdetermined modeluncertain measurementsparticle swarm optimization |
| spellingShingle | Lawrence K. Letting Yskandar Hamam Adnan M. Abu-Mahfouz Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization Water water demand estimation demand multipliers underdetermined model uncertain measurements particle swarm optimization |
| title | Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization |
| title_full | Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization |
| title_fullStr | Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization |
| title_full_unstemmed | Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization |
| title_short | Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization |
| title_sort | estimation of water demand in water distribution systems using particle swarm optimization |
| topic | water demand estimation demand multipliers underdetermined model uncertain measurements particle swarm optimization |
| url | https://www.mdpi.com/2073-4441/9/8/593 |
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