Case Study: Improvements to a Real-Time Network Modelling Framework

S. M.Masud Rana, Paulo José Oliveira, Tian Qin, Dominic L. Boccelli

Research output: Contribution to conferencePaperpeer-review


Short-term water demand forecasts are valuable for distribution system operators controlling the production, storage and delivery of drinking water. In certain problems, such as real-time pump scheduling, the cycle of data acquisition, model computation, and decision-making is time-sensitive, and requires an automatic procedure to handle the transfer of information between data source(s), forecasting model(s), and the operator. Recent development of a composite demand-hydraulic model integrates a demand time series model with a hydraulic network model to estimate and forecast demands using measurements typically available to water utilities. The application to a real-world network model with approximately 12,000 demand nodes and six flow measurements resulted in good representation of the observed flow rates. However, the performance of the demand-hydraulic algorithm, and subsequent analysis, has demonstrated limitations in two aspects of the demand estimation and forecasting framework: the temporal representation of the estimated demands, and the clustering approach needed to reduce the scale of the parameter estimation problem. The current research will present preliminary results associated with data-driven approaches for representing the temporal demands and application of alternative clustering algorithms to improve the overall demand estimation process.

Original languageEnglish (US)
StatePublished - 2017
Externally publishedYes
Event15th International Conference on Computing and Control for the Water Industry, CCWI 2017 - Sheffield, United Kingdom
Duration: Sep 5 2017Sep 7 2017


Conference15th International Conference on Computing and Control for the Water Industry, CCWI 2017
Country/TerritoryUnited Kingdom


  • Clustering
  • Real-time modeling
  • Time series

ASJC Scopus subject areas

  • Water Science and Technology
  • Computer Science Applications


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