Abstract
This paper focuses on water demand forecasting for predictive control of Drinking Water Networks (DWN) in the short term by using Gaussian Process (GP). For the predictive control strategy, system states in a finite horizon are generated by a DWN model and demands are regarded as system disturbances. The goal is to provide a demand estimation within a given confidence interval. For the sake of obtaining a desired forecasting performance, the forecasting process is carried out in two parts: the expected part is forecasted by Double-Seasonal Holt-Winters (DSHW) method and the stochastic part is forecasted by GP method. The mean value of water demand is firstly estimated by DSHW while GP provides estimations within a confidence interval. GP is applied with random inputs to propagate uncertainty at each step. Results of the application of the proposed approach to a real case study based on the Barcelona DWN have shown that the general goal has been successfully reached.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
AGBAR: Aguas de Barcelona, S. A. Company which manages the drinking water transport and distribution in Barcelona (Spain).
References
Bakker, M., Vreeburg, J.H.G., van Schagen, K.M., Rietveld, L.C.: A fully adaptive forecasting model for short-term drinking water demand. Environ. Model. Softw. 48(0), 141–151 (2013)
Blanch, J., Quevedo, J., Saludes, J., Puig, V.: Short-term demand forecasting for operational control of thebarcelona water transport network. In: Conferencia Nacional de Jóvenes Profesionales del Agua deEspaña, pp. 1–10. Barcelona (2010)
Christiaanse, W.R.: Short-term load forecasting using general exponential smoothing. IEEE Trans. Power Apparatus Syst. 90(2), 900–911 (1971)
Deisenroth, M.P.: Efficient reinforcement learning using Gaussian processes. Ph.D. thesis, Karlsruhe Institute of Technology (2010)
Fung, Y., Rao Tummala, V.: Forecasting of electricity consumption: a comparative analysis ofregression and artificial neural network models. In: 2nd International Conference on Advances in Power SystemControl, Operation and Management, vol. 2, pp. 782–787 (1993)
Grosso, J.M., Ocampo-MartÃnez, C., Puig, V., Joseph, B.: Chance-constrained model predictive control for drinking water networks. J. Process Control 24(5), 504–516 (2014)
Harrison, P.J.: Exponential smoothing and short-term sales forecasting. Manage. Sci. 13(11), 821–842 (1967)
Hayati, M., Shirvany, Y.: Artificial neural network approach for short term load forecasting for illam region. World Acad. Sci. Eng. Technol. 22, 280–284 (2007)
Lourenco, J.M., Santos, P.J.: Short-term load forecasting using a gaussian process model: the influence of a derivative term in the input regressor. Intell. Decis. Technol. 6(4), 273–281 (2012)
Maciejowski, J.M., Yang, X.: Fault tolerant control using gaussian processes and model predictivecontrol. In: 2013 Conference on Control and Fault-Tolerant Systems (SysTol), pp. 1–12. Nice (2013)
Msiza, I.S., Nelwamondo, F.V., Marwala, T.: Water demand prediction using artificial neural networks and support vector regression. Digital Intell. 3(11), 1–8 (2008)
Ocampo-MartÃnez, C., Puig, V., Cembrano, G., Quevedo, J.: Application of MPC strategies to the management of complex networks of the urban water cycle. IEEE Control Syst. Mag. 33(1), 15–41 (2013)
Pawlowski, A., Guzman, J.L., Rodriguez, F., Berenguel, M., Normey-Rico, J.E.: Predictive control with disturbance forecasting for greenhousediurnal temperature control. In: The 18th IFAC World Congress, pp. 1779–1784. Milano (2011)
Quinonero-Candela, J., Girard, A., Rasmussen, C.E.: Prediction at an uncertain input for gaussian processes and relevancevector machines application to multiple-step ahead time-series forecasting. Technical report, University of Glasgow, Department of ComputingScience (2002)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. ISBN 026218253X. the MIT Press, Massachusetts Institute of Technology (2006)
Samarasinghe, M., Al-Hawani, W.: Short-term forecasting of electricity consumption using gaussian processes. Master’s thesis, University of Agder (2012)
Taylor, J.W.: Short-term electricity demand forecasting using double seasonalexponential smoothing. J. Oper. Res. Soc. 54(8), 799–805 (2003)
Wang, Y.: Model predictive control for drinking water networks based on gaussian processes. Master’s thesis, Technical University of Catalonia (2014)
Acknowledgments
This work is partially supported by the research projects CICYT SHERECS DPI-2011-26243 and ECOCIS DPI-2013-48243-C2-1-R, both of the Spanish Ministry of Education, by EFFINET grant FP7-ICT-2012-318556 of the European Commission and by AGAUR Doctorat Industrial 2013-DI-041. Ye Wang also thanks China Scholarship Council for providing postgraduate scholarship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Y., Ocampo-MartÃnez, C., Puig, V., Quevedo, J. (2016). Gaussian-Process-Based Demand Forecasting for Predictive Control of Drinking Water Networks. In: Panayiotou, C., Ellinas, G., Kyriakides, E., Polycarpou, M. (eds) Critical Information Infrastructures Security. CRITIS 2014. Lecture Notes in Computer Science(), vol 8985. Springer, Cham. https://doi.org/10.1007/978-3-319-31664-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-31664-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31663-5
Online ISBN: 978-3-319-31664-2
eBook Packages: Computer ScienceComputer Science (R0)