OPEN ACCESS
The development of new monitoring and control technologies has changed the management of water distribution systems (WDSs) from passive to more efficient by generating the notion of a Smart Water Network (SWN). These technologies provide the real-time monitoring of the urban water networks and, consequently, improve the faults diagnosis. This article presents SunRise – Smart Water: a demonstration site of a SWN. The large-scale demonstrator is well equipped by a set of sensors that measure continuously the hydraulic characteristics of the system. The flow readings collected from the sensors are statistically analysed using the k-means algorithm evaluated by the entropy scorer to regroup clusters with same behaviour in terms of water usage. In parallel, the smart monitoring of the water network in the demo site has allowed for leakage detection either by following the water consumption patterns or by computing the minimum night flow (MNF).
smart water network, automatic meter readings, monitoring, leakage, minimum night flow
[1] SENSUS, Water 20/20 – Bringing Smart Water Networks Into Focus, 2012.
[2] Chourabi, H., Nam, T., Walker, S. & Gil-Garcia, J.R., Understanding smart cities: an integrative framework. in 45th Hawaii International Conference on System Science, 2012.
[3] Fang, X., Misra, S., Xue,G. & Yang, D., Smart grid—the new and improved power grid: a survey. Communications Surveys & Tutorials, IEEE, 14(4), pp. 944–980, 2012. http://dx.doi.org/10.1109/SURV.2011.101911.00087
[4] Boulos, P.F. & Wiley, A.N., Can we make water systems smarter? Opflow, 39(3), pp. 20–22, 2013. http://dx.doi.org/10.5991/OPF.2013.39.0015
[5] Cahn, A., Shaping the architecture of smart water networks, in water & sewerage journal, 3, pp. 13–15, 2013.
[6] Thornton, J., Sturm, R. & Kunkel, G., Water loss control. 2008: McGraw Hill Professional, 2nd edn., 632
[7] Morrison, J., Managing leakage by district metered access: a practical approach. Water, 21, pp. 44–46, 2010.
[8] Alkasseh, J.A., Adlan, M.N., Abustan, I., Aziz, H.A. & Mohamad Hanif, A.B., Applying minimum night flow to estimate water loss using statistical modeling: a case study in Kinta Valley, Malaysia. Water Resources Management, 27(5), pp. 1439–1455, 2013. http://dx.doi.org/10.1007/s11269-012-0247-2
[9] Kaufman, L. & Rousseeuw, P.J., Finding groups in data: an introduction to cluster analysis, John Wiley & Sons, 344, 2009.
[10] Kailing, K., Kriegel, H.P., Pryakhin, A. & Schubert, M., Clustering multi-represented objects with noise. In Advances in Knowledge Discovery and Data Mining, Springer, pp. 394–403, 2004. http://dx.doi.org/10.1007/978-3-540-24775-3_48
[11] Ay, M. & Kisi, O., Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques. Journal of Hydrology, 511, pp. 279–289, 2014. http://dx.doi.org/10.1016/j.jhydrol.2014.01.054