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

Author

Listed:
  • Shipeng Chu

    (Zhejiang University)

  • Tuqiao Zhang

    (Zhejiang University)

  • Xinhong Zhou

    (Zhejiang University)

  • Tingchao Yu

    (Zhejiang University)

  • Yu Shao

    (Zhejiang University)

Abstract

Real-time modeling of a 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 in the modeling of large-scale networks. The Jacobian matrix computation and Hessian matrix inversion are the main processes that dominate the computation time. To address this problem, an approach for shortening the computation time for real-time demand estimation in large-scale network is proposed. This approach allows the Jacobian matrix to be efficiently computed based on solving a system of linear equations, and a Hessian matrix inversion method based on matrix partitioning and the iterative Woodbury-Matrix-Identity Formula is proposed. The developed approach is applied to a large-scale network, in which the number of nodal water demands is 12523, and the number of measurements ranges from 10 to 2000. The results show that the time consumptions for the Jacobian computation and Hessian matrix inversion are within 465.3 ms and 1219.0 ms, respectively. The time consumption is significantly shortened compared with the existing approach, especially for nodal water demand estimation in large-scale WDSs.

Suggested Citation

  • Shipeng Chu & Tuqiao Zhang & Xinhong Zhou & Tingchao Yu & Yu Shao, 2022. "An Efficient Approach for Nodal Water Demand Estimation in Large-scale Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 491-505, January.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:2:d:10.1007_s11269-021-03024-w
    DOI: 10.1007/s11269-021-03024-w
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    References listed on IDEAS

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    1. Gustavo Meirelles & Daniel Manzi & Bruno Brentan & Thaisa Goulart & Edevar Luvizotto, 2017. "Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(13), pages 4339-4351, October.
    2. Reza Moasheri & Mohammadreza Jalili-Ghazizadeh, 2020. "Locating of Probabilistic Leakage Areas in Water Distribution Networks by a Calibration Method Using the Imperialist Competitive Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 35-49, January.
    3. L. Berardi & O. Giustolisi, 2021. "Calibration of Design Models for Leakage Management of Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2537-2551, June.
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    Cited by:

    1. Yumin Wang, 2023. "Joint-Probabilistic Double-Sided Random Interval Programming for Booster Optimization in Water Distribution Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 501-520, January.

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