CCWi2017: F131 'A Python Tool for Generating Synthetic Demand Scenarios'
journal contributionposted on 01.09.2017 by Simone Santopietro, Francesco Granata, Carla Tricarico, Giovanni de Marinis, Rudy Gargano
Any type of content formally published in an academic journal, usually following a peer-review process.
Demand modelling has a great impact on the outcome of hydraulic simulations of water distribution systems. Its stochastic nature has to be considered in order to obtain more reliable results. A stochastic approach, based on a mixed probability distribution, able to model residential water demand has been implemented in Python. This distribution considers both the event of null and not null water demand and allows to estimate the statistical parameters, solely on the number of supplied users and on the related average demand pattern. This approach is well suited to be used in hydraulic simulation workflows (e.g. using the EPANET toolkit) and a Python implementation could encourage a more refined demand characterization also in non-scientific environments.