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International Conference on Intelligent Decision Technologies

IDT 2017: Intelligent Decision Technologies pp 171–180Cite as

Dealing with Seasonality While Forecasting Urban Water Demand

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 39))

Abstract

Forecasting water demand is required for the efficient controlling of pressure within water distribution systems, leading to the reduction of water leakages. For the purpose of this research, it is assumed that the control of water pressure is performed by pressure reduction valves (PRVs) working in the open loop mode. This means that water pressure is controlled on the basis of the daily water demand profile, a 24-step ahead forecasting of hourly time series. A key issue in such time series that affects the effectiveness of its forecasting is seasonality. Three different techniques to deal with seasonality are investigated in this paper: auto-regressive, differentiation, and the application of dummy variables. This paper details a comparative study of these three techniques with respect to water demand time series and different predictive models. We show that an approach based on dummy variables and linear regression outperforms the other methods.

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References

  1. Bianco, V., Manca, O., Nardini, S.: Electricity consumption forecasting in Italy using linear regression models. Energy 34(9), 1413–1421 (2009)

    Article  Google Scholar 

  2. Billings, R.B., Jones, C.V.: Forecasting Urban Water Demand. American Water Works Association, Denver (2008)

    Google Scholar 

  3. Cortez, P., Rocha, M., Neves, J.: Genetic and evolutionary algorithms for time series forecasting. In: Proceedings of the IEA/AIE-2001: The 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 393–402, 4–7 June 2001

    Google Scholar 

  4. Cowpertwait, P.S.P., Metcalfe, A.V.: Introductory Time Series with R, 1st edn. Springer Publishing Company, Incorporated (2009)

    MATH  Google Scholar 

  5. Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. J. Bus. Econ. Stat. 13(3), 253–63 (1995)

    Google Scholar 

  6. Dudek, G.: Forecasting time series with multiple seasonal cycles using neural networks with local learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds.) Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, vol. 7894, pp. 52–63. Springer, Berlin Heidelberg (2013)

    Chapter  Google Scholar 

  7. Herrera, M., Torgo, L., Izquierdo, J., Prez-Garca, R.: Predictive models for forecasting hourly urban water demand. J. Hydrol. 387(12), 141–150 (2010)

    Article  Google Scholar 

  8. House-Peters, L.A., Chang, H.: Urban water demand modeling: review of concepts, methods, and organizing principles. Water Res. Res. 47(5), n/a-n/a (2011)

    Google Scholar 

  9. Joby Boxall, C.M.C.P. (ed.): On-line Hydraulic State Estimation in Urban Water Networks Using Reduced Models (2009)

    Google Scholar 

  10. Kim, J., Shin, G., Choi, D.: Develpment of water demand forecasting simulator and its performance assessment. In: WDSA 2012: 14th Water Distribution Systems Analysis Conference, 24–27 Sept 2012 in Adelaide, pp. 1418–1423. South Australia. Barton, A.C.T.: Engineers Australia (2012)

    Google Scholar 

  11. KNIME: Professional open-source software. http://www.knime.org

  12. Liu, J.Q., Zhang, T.Q., Yu, S.K.: Chaotic phenomenon and the maximum predictable time scale of observation series of urban hourly water consumption. J. Zhejiang Univ. Sci. 5(9), 1053–1059 (2004)

    Google Scholar 

  13. Mavromatidis, L.E., Bykalyuk, A., Lequay, H.: Development of polynomial regression models for composite dynamic envelopes thermal performance forecasting. Appl. Energy 104, 379–391 (2013)

    Article  Google Scholar 

  14. Package, R.: http://www.r-project.org

  15. Pulido-Calvo, I., Montesinos, P., Roldn, J., Ruiz-Navarro, F.: Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems. Biosyst. Eng. 97(2), 283–293 (2007)

    Article  Google Scholar 

  16. Shmueli, G.: Practical Time Series Forecasting. Statistics.com LLC, 2nd edn. (2011)

    Google Scholar 

  17. Thornton, J., Sturm, R., Kunkel, G.: Water Loss Control, 2nd edn. The McGraw-Hill Companies, Inc., New York (2008)

    Google Scholar 

  18. Ulanicki, B., Bounds, P., Rance, J., Reynolds, L.: Open and closed loop pressure control for leakage reduction. Urban Water 2(2), 105–114 (2000), developments in water distribution systems

    Google Scholar 

  19. Zhou, S., McMahon, T., Walton, A., Lewis, J.: Forecasting daily urban water demand: a case study of melbourne. J. Hydrol. 236(34), 153–164 (2000)

    Article  Google Scholar 

Download references

Acknowledgments

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.

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Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, Sosnowiec, Poland

    Wojciech Froelich

Authors
  1. Wojciech Froelich
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Correspondence to Wojciech Froelich .

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Editors and Affiliations

  1. FCT, Universidade Nova de Lisboa, Caparica, Portugal

    Rui Neves-Silva

  2. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

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Froelich, W. (2015). Dealing with Seasonality While Forecasting Urban Water Demand. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-19857-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19856-9

  • Online ISBN: 978-3-319-19857-6

  • eBook Packages: EngineeringEngineering (R0)

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