Forecasting Domestic Water Consumption Using Bayesian Model

  • Wojciech FroelichEmail author
  • Ewa Magiera
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)


In this paper, we address the problem of forecasting domestic water consumption. A specific feature of the forecasted time series is that water consumption occurs at random time steps. This substantially limits the application of the standard state-of-the art forecasting methods. The other existing forecasting models dedicated to predicting water consumption in households rely on data collected from questionnaires or diaries, requiring additional effort for gathering data. To overcome those limitations, we propose in this paper a Bayesian model to be applied for the forecasting of the domestic water consumption time series. The proposed theoretical approach has been tested using real-world data gathered from an anonymous household.


Forecasting time series Domestic water consumption Bayesian networks 



The work was supported by ISS-EWATUS project which has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 619228.


  1. 1.
    Abramson, B., Brown, J., Edwards, W., Murphy, A., Winkler, R.L.: Hailfinder: a Bayesian system for forecasting severe weather. Int. J. Forecast. 12(1), 57–71 (1996)CrossRefGoogle Scholar
  2. 2.
    Bennett, C., Stewart, R.A., Beal, C.D.: Ann-based residential water end-use demand forecasting model. Expert Syst. Appl. 40(4), 1014–1023 (2013)CrossRefGoogle Scholar
  3. 3.
    Biondi, D., Luca, D.D.: Performance assessment of a bayesian forecasting system (bfs) for real-time flood forecasting. J. Hydrol. 479, 51–63 (2013)CrossRefGoogle Scholar
  4. 4.
    Carragher, B.J., Stewart, R.A., Beal, C.D.: Quantifying the influence of residential water appliance efficiency on average day diurnal demand patterns at an end use level: A precursor to optimised water service infrastructure planning. Resources, Conservation and Recycling, pp. 81–90 (2012)Google Scholar
  5. 5.
    Fox, C., McIntosh, B., Jeffrey, P.: Classifying households for water demand forecasting using physical property characteristics. Land Use Policy 26(3), 558–568 (2009)CrossRefGoogle Scholar
  6. 6.
    Froelich, W.: Forecasting daily urban water demand using dynamic gaussian bayesian network. In: Proceedings of the BDAS 2015, pp. NA–NA. Springer, Ustro, Poland (2015)Google Scholar
  7. 7.
    Froelich, W., Papageorgiou, E.I.: Extended evolutionary learning of fuzzy cognitive maps for the prediction of multivariate time-series. In: Fuzzy Cognitive Maps for Applied Sciences and Engineering—From Fundamentals to Extensions and Learning Algorithms, pp. 121–131. Springer (2014)Google Scholar
  8. 8.
    Froelich, W., Salmeron, J.L.: Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate, interval-valued time series. Int. J. Approx. Reason. 55(6), 1319–1335 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Froukh, M.: Decision-support system for domestic water demand forecasting and management. Water Res. Manag. 15(6), 363–382 (2001)CrossRefGoogle Scholar
  10. 10.
    Fuss, N.A.: Determination and verification of possible resource savings in manual dishwashing, Ph.D. thesis. Rheinische Friedrich-Wilhelms-Universitt Bonn, 1st edn. (2011)Google Scholar
  11. 11.
    Gato, S.: Forecasting Urban Residential Water Demand, Ph.D. thesis. School of Civil, Environmental and Chemical Engineering Science, RMIT University, 1st edn. (2006)Google Scholar
  12. 12.
    IBM: Smart Water Pilot Study Report. IBM Research, 1st edn. (2011)Google Scholar
  13. 13.
    Inman, D.: The use of Bayesian networks to facilitate implementation of water demand management strategies, Ph.D. thesis. Cranfield University, School of Applied Sciences (2008)Google Scholar
  14. 14.
    Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer (2001)Google Scholar
  15. 15.
    Jorgensen, B., Graymore, M., O’Toole, K.: Household water use behavior: an integrated model. J. Env. Manag. 91(1), 227–236 (2009)CrossRefGoogle Scholar
  16. 16.
    Krants, H.: Matter that Matters.A study of household routines in a process of changing water and sanitation arrangements. Department of Water and Environmental Studiesy, 1st edn. (2005)Google Scholar
  17. 17.
    Linkola, L.: Behaviorally based modeling of domestic water use. Masters thesis. Leiden Unversity, Delft Univesity of Technology, 1st edn. (2011)Google Scholar
  18. 18.
    Magiera, E., Froelich, W.: Application of bayesian networks to the forecasting of daily water demand. In: Proceedings of the KES IDT-15, Sorrento, Italy. pp. NA–NA. Springer, 17–19 July 2015Google Scholar
  19. 19.
  20. 20.
    Richter, C., Stamminger, R.: Water consumption in the kitchen a case study in four european countries. Water Res. Manag. 26(6), 1639–1649 (2012)CrossRefGoogle Scholar
  21. 21.
    Scutari, M.: Bayesian network structure learning, parameter learning and inferenceg (2014).
  22. 22.
    Vlachopoulou, M., Chin, G., Fuller, J.C., Lu, S., Kalsi, K.: Model for aggregated water heater load using dynamic bayesian networks. In: Proceedings of the DMIN’12 International Conference on Data Mining, pp. 1–7 (2012)Google Scholar

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Authors and Affiliations

  1. 1.Institute of Computer Science, University of SilesiaSosnowiecPoland

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