Considering the Mutual Dependence of Pulse Duration and Intensity in Models for Generating Residential Water Demand

Abstract

6This paper proposes the application of three different methods for preserving the correlation between 7 duration and intensity of synthetically generated water demand pulses. The first two methods, i.e., the 8 Iman and Canover method and the Gaussian copula respectively, are derived from known statistical 9 approaches, though they had never been applied to the context of demand pulse generation. The third is a 10 novel methodology developed in this work and is a variation in the Gaussian copula approach. Poisson

On the choice of the demand and hydraulic modeling approach to WDN real‐time simulation

“…In order to generate demand at a generic node, a model similar to that proposed by Creaco et al . [] is considered. In this model, the frequency of residential water use follows a nonstationary Poisson arrival process.…”

“…After generating pulse arrivals, T and I of the generic pulse have to be generated using a bivariate probability distribution model [ Creaco et al ., ], in order to take account of the duration/intensity correlation. Compared to the model of Creaco et al .…”

“…Compared to the model of Creaco et al . [], in which the bivariate lognormal distribution was implemented, the bivariate beta distribution is used in this case because it enables lower bounds ( T min and I min ) and upper bounds ( T max and I max ) to be prescribed on T and I , as is the case with real observations. The bivariate beta distribution requires definition of nine parameters that is the four parameters related to T ( α T , β T , T min , and T max ), the four parameters related to I ( α I , β I , I min , and I max ) and correlation ρ between T and I .…”

“…BUA, instead, aims to reconstruct nodal demands starting from individual users' consumption, which is represented as a stochastic variable [ Walski et al ., ]. To this end, demands can be assessed by temporal aggregation of water demand pulses produced by pulse generation models [e.g., Buchberger and Wu , ; Buchberger et al ., ; Blokker et al ., ; Creaco et al ., ]. Indeed, the scientific evidence that nodal demands take the shape of pulses at the temporal scale of 1 s [ Buchberger et al ., ; Blokker et al ., ] makes BUA the more consistent approach to represent WDN operation.…”

A filtering algorithm for high-resolution flow traces to improve water end-use analysis

“…A more accurate characterization of residential demand is a powerful tool that gives effective support to water demand modelling and management (Buchberger and Wells, 1996;Guercio et al, 2001;Alvisi et al, 2003;García et al, 2004;Blokker et al, 2010;Creaco et al, 2015Creaco et al, , 2016. The development of high resolution smart meters facilitates a more accurate and detailed characterization of water consumption profiles (Cominola et al, 2015).…”