A Tool for Daily Demand Pattern Generation

9 pages•Published: September 20, 2018

Rudy Gargano, Carla Tricarico, Simone Santopietro, Giovanni de Marinis and Guglielmo Silvagni

Abstract

A correct water demand characterization is at the base of a reliable water distribution system simulation. The stochastic nature of water demand is well established and thus has to be addressed. In the present work a methodology to generate synthetic demand patterns interpolating known points by means of piecewise interpolation has been implemented in Python. Subsequently a stochastic approach has been applied to the interpolated demand patterns, which is based on a mixed probability distribution. Such approach considers the dual nature of water demand as continuous and discrete random variable, in order to contemplate both the event of it being null and not null. The needed parameters are obtainable through simple equations depending solely on the number of served users.

Keyphrases: daily demand pattern, probability approach, python

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 746-754.

Links:	https://easychair.org/publications/paper/R14g
	https://doi.org/10.29007/l165

BibTeX entry

@inproceedings{HIC2018:Tool_Daily_Demand_Pattern,
  author    = {Rudy Gargano and Carla Tricarico and Simone Santopietro and Giovanni de Marinis and Guglielmo Silvagni},
  title     = {A Tool for Daily Demand Pattern Generation},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/R14g},
  doi       = {10.29007/l165},
  pages     = {746-754},
  year      = {2018}}

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