A fully adaptive forecasting model for short-term drinking water demand

  • Authors:

  • M. Bakker;J. H. G. Vreeburg;K. M. Van Schagen;L. C. Rietveld

  • Affiliations:

  • Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands and Royal HaskoningDHV B.V., P.O. Box 1132, 3800 BC Amersfoort, The Neth ...;Sub-Department of Environmental Technology, Wageningen University, P.O. Box 17, 6700 AA Wageningen, The Netherlands and KWR Watercycle Research Institute, P.O. Box 1072, 4330 BB Nieuwegein, The Ne ...;Royal HaskoningDHV B.V., P.O. Box 1132, 3800 BC Amersfoort, The Netherlands;Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2013

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Abstract

For the optimal control of a water supply system, a short-term water demand forecast is necessary. We developed a model that forecasts the water demand for the next 48 h with 15-min time steps. The model uses measured water demands and static calendar data as single input. Based on this input, the model fully adaptively derives day factors and daily demand patterns for the seven days of the week, and for a configurable number of deviant day types. Although not using weather data as input, the model is able to identify occasional extra water demand in the evening during fair weather periods, and to adjust the forecast accordingly. The model was tested on datasets containing six years of water demand data in six different areas in the central and Southern part of Netherlands. The areas have all the same moderate weather conditions, and vary in size from very large (950,000 inhabitants) to small (2400 inhabitants). The mean absolute percentage error (MAPE) for the 24-h forecasts varied between 1.44 and 5.12%, and for the 15-min time step forecasts between 3.35 and 10.44%. The model is easy to implement, fully adaptive and accurate, which makes it suitable for application in real time control.