Abstract. Energy use in drinking Water Supply System represents an important part of the global energy consumption across all sectors. This portion is expected to raise, due to the raising demand and the recourse to unconventional water resources. For the water utilities, most of their operating costs are related to energy consumptions, especially the consumption of pumping systems. The main objective of this study is to produce a model which reflects the real behaviour of a pumping system to help in taking decisions on which pump to use First and which one to replace in case of a limited renovation. In order to do so, Multiple Linear regression was adopted to model the ratio kWh/m3 produced depending on the input parameters. The final model describes in a good manner the phenomenon (R2 = 0.91), so it can be a good estimator as the calculated ratio is close to the experimental one. The Novelty of this approach is to have a model which takes into account the real behaviour of the system whereas most of the studies focus on the pump scheduling problem.
Energy use in water supply systems represents a consequent part of global energy consumption across all sectors. This consumption is expected to rise, due to the increasing demand and the recourse to unconventional water resources. Regarding water utilities, most of their operating costs are related to energy consumption, especially pumping systems consumption. In this context, the main objective of this study was to model accurately by using data statistical analysis the energy consumption of pumping systems in order to optimize the whole water supply system, thus improving its efficiency, especially in the case of a limited renovation. For this purpose, Multiple Linear Regression was fitted to model the produced kWh/m 3 ratio costs according to the following Key-parameters associated to drinking pumping stations: i) active and reactive energies; ii) the daily produced water volume; iii) the power factor (Cosφ); iiii) and the operating time of each pump. The final model describes accurately the consumption per cubic meter produced with R-square statistic reaching 0.91 and value standard error is close to 5% were found. Therefore, this model could be considered a good estimator for the calculated ratio, which was close to the experimental one. In addition, this approach considers the system in the Real-Time-Data behavior, while most of the comparable studies focus on the pump scheduling problem estimator for the calculated ratio which was close to the experimental one.
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