The forecast water consumption values are the most critical input data in the pump schedule optimization of water distribution systems. The aim of this paper is to present a simple technique which is able to estimate the mean consumption and its distribution for a given demand zone with an hourly resolution. Simplicity (low computational cost) is advantageous since the forecast model needs to be run for any optimization computation. The proposed technique uses a recorded hourly consumption database and consists of two steps. First, the database content is automatically grouped based on the similarity of the elements (more precisely, their normality). This step is time-consuming but is performed only once for a given database independently of optimization. The second step – which is quick but has to be performed before the actual optimization – makes use of this grouping for forecasting mean value and standard deviation. The proposed technique provides hourly water consumption predictions independently; that is, the neighbouring hours do not effect each other, which prevents the accumulation of prediction errors. The daily overall consumption is computed a posteriori. The test results presented in this paper prove the applicability of the technique for real-life problems. Moreover, it is demonstrated that the confidence interval provided by the technique includes the actual measured data.

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