The behavior of residential users in the request of water influences the demand patterns which characterize the water consumption at each node of the network during the time. This stochastic variable influences greatly the performance of hydraulic networks and a better understanding of residential water consumption variability might allow for a more effective design or management of Water Distribution Systems. In order to contribute to this analysis, stochastic models are herein defined and proposed for representing demand variation, especially at the peak condition. This study is based on an experimental approach, comparing empirical data collected in several real water distribution networks which refer to different habits and number of users. Statistical inferences on large data samples have allowed defining probabilistic models and relative parameters which characterize the maximum water consumption at network nodes. The study has been developed considering both the deterministic cyclical component of the demand variability and its uncertain component that accommodates the random nature of the water requirement. Results obtained have shown that Gumbel and Log-Normal distributions best represent the peak water demand for all the monitoring systems considered.

Residential Water Demand - daily trends

GARGANO, Rudy;TRICARICO, Carla;DE MARINIS, Giovanni
2012

Abstract

The behavior of residential users in the request of water influences the demand patterns which characterize the water consumption at each node of the network during the time. This stochastic variable influences greatly the performance of hydraulic networks and a better understanding of residential water consumption variability might allow for a more effective design or management of Water Distribution Systems. In order to contribute to this analysis, stochastic models are herein defined and proposed for representing demand variation, especially at the peak condition. This study is based on an experimental approach, comparing empirical data collected in several real water distribution networks which refer to different habits and number of users. Statistical inferences on large data samples have allowed defining probabilistic models and relative parameters which characterize the maximum water consumption at network nodes. The study has been developed considering both the deterministic cyclical component of the demand variability and its uncertain component that accommodates the random nature of the water requirement. Results obtained have shown that Gumbel and Log-Normal distributions best represent the peak water demand for all the monitoring systems considered.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/19938
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