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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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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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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