Bruno M. Brentan^1*, Lubienska C. L. J. Ribeiro², Edevar Luvizotto Jr.¹, Danilo C. Mendonça², José M. Guidi³

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¹ Water Recourses Department, School of Civil Engineering, Unicamp, Campinas, Brazil.
² School of Technology, Unicamp, Limeira, Brazil.
³ Company of Water Supply of Sao Paulo, Franca, Brazil.
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Abstract: The forecasting of the demand applied to water supply systems has been an important tool to realize time control. The use of the time series to do the forecasting of the demand is the main way that has been used by researchers. By this way, the need of a complete time demand series increases. This work presents two ways to reconstruct the water demand time series synthetically, using the Average Reconstruction Method and Fourier Method. Both the methods were considered interesting to do the synthetic reconstruction and able to complete the time series, but the Fourier Method showed better results and a better fitness to approximation of the water consumption pattern.

Keywords: Water Demand Forecasting, Synthetic Reconstruction, Water Supply Systems

Cite this paper: Brentan, B. , Ribeiro, L. , Luvizotto Jr., E. , Mendonça, D. and Guidi, J. (2014) Synthetic Reconstruction of Water Demand Time Series for Real Time Demand Forecasting. Journal of Water Resource and Protection, 6, 1437-1443. doi: 10.4236/jwarp.2014.615132.

References

[1]   Herrera, M., Torgo, L., Izqueirdo, J. and Perez-Garcia, R. (2010) Predictive Models for Forecasting Hourly Urban Water Demand. Journal of Hidrology, 387, 141-150.
http://dx.doi.org/10.1016/j.jhydrol.2010.04.005

[2]   Adamowski, J. (2008) Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks. Journal of Water Resources Planning and Management, 134, 119-128.
http://dx.doi.org/10.1061/(ASCE)0733-9496(2008)134:2(119)

[3]   Perry, P.F. (1981) Demand Forecasting in Water Supply Systems. Journal of the Hydraulics Division, 107, 1077-1087.

[4]   Santos, C.C. (2011) Forecast of Water Demand in the Metropolitan Region of San Paul with Artificial Neural Networks and Socio-Environmental and Weather Conditions. Doctor Theses, EPUSP, San Paul.

[5]   Silva, C.S. (2003) Multivariate Forecast of Water Demand in Urban Water Supply Systems. Previsao multivariada de demanda de água em sistemas urbanos de abastecimento. Doctor Theses, Unicamp, Campinas.

[6]   An, A., Shan, C.C., Cercone, N. and Ziarko, W. (1995) Discovering Rules from Data for Water Demand Prediction. Proceedings in the Workshop on Machine Learning and Expert System (IJCAI’95), 187-202.

[7]   Lucas, S.A., Coombes, P.J. and Sharma, A.K. (2010) The Impact of Diurnal Water Use Patterns, Demand Management and Rainwater Tanks on Water Supply Network Design. Water Science & Technology: Water Supply—WSTWS, 10, 69-80.

[8]   Magnano, L. and Boland, J.W. (2007) Generation of Synthetic Sequences of Electricity Demand. Application at South Australia. Energy, 32, 2230-2243.
http://dx.doi.org/10.1016/j.energy.2007.04.001

[9]   Luvizotto, E.J. (1992) Analytical Representation of Characteristic Curves of Hydraulic Machines for Use in Computer Simulations routines. MSc. Thesis, EPUSP, San Paul.