Real Time Water Demand Estimation in Water Distribution System

2008 ◽  
Author(s):  
Feng Shang ◽  
James G. Uber ◽  
Bart G. van Bloemen Waanders ◽  
Dominic Boccelli ◽  
Robert Janke
Keyword(s):  
Real Time ◽  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Demand Estimation

scholarly journals Optimal Node Grouping for Water Distribution System Demand Estimation

Water ◽  
2016 ◽  
Vol 8 (4) ◽  
pp. 160 ◽  
Author(s):  
Donghwi Jung ◽  
Young Choi ◽  
Joong Kim
Keyword(s):  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Demand Estimation ◽  
Optimal Node ◽  
Node Grouping

scholarly journals A Wireless Sensor Network Model for E-Metering and Fault Identification in Smart Water Distribution Systems

2019 ◽  
Vol 8 (10S) ◽  
pp. 227-232
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Network Model ◽  
Distribution System ◽  
Water Distribution ◽  
Large Scale ◽  
Water Distribution System ◽  
Wireless Sensor ◽  
Smart Water

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB.

Real-time ArcGIS and heterotrophic plate count based chloramine disinfectant control in water distribution system

2015 ◽  
Vol 68 ◽  
pp. 812-820 ◽  
Author(s):  
Xiaohui Bai ◽  
Xinghua Zhi ◽  
Huifeng Zhu ◽  
Mingqun Meng ◽  
Mingde Zhang
Keyword(s):  
Real Time ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Plate Count ◽  
Heterotrophic Plate Count

scholarly journals An Efficient Approach For the Nodal Water Demand Estimation in Large-Scale Water Distribution Systems

2021 ◽  
Author(s):  
Shipeng Chu ◽  
Tuqiao Zhang ◽  
Xinhong Zhou ◽  
Tingchao Yu ◽  
Yu Shao
Keyword(s):  
Real Time ◽  
Water Demand ◽  
Water Distribution ◽  
Large Scale ◽  
Jacobian Matrix ◽  
Hessian Matrix ◽  
Demand Estimation ◽  
Matrix Inversion ◽  
Large Scale Network ◽  
Scale Network

Abstract 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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