IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v17y2003i3p197-222.html
   My bibliography  Save this article

A Stochastic Model for Representing Drinking Water Demand at Residential Level

Author

Listed:
  • Stefano Alvisi
  • Marco Franchini
  • Alberto Marinelli

Abstract

In this study, attention is initially focussed on modelling finely sampled (1 min) residential water demand time series. Subsequently, the possibility of simulating the water demand time series relevant to different time intervals and many users is analysed by using an aggregation approach. A cluster Neyman-Scott stochastic process (NSRP) is proposed to represent the residential water demand and a parameterisation procedureis implemented to respect the cyclical behaviour usually observed in any working day. A validation is performed on the basis of the one-minute datacollected on the water distribution system of Castelfranco Emilia located in the province of Modena (I). The elaborations performed show the validity both of the NSRP model and the parameterisation procedure proposedto represent the residential demand with fine time intervals (up to 5–10 min). On the other hand, when a procedure of aggregation is applied to represent the water demand of a high number of users, the results are nolonger satisfactory since only the mean is preserved while the other statistics, and in particular the variance, are underestimated. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Stefano Alvisi & Marco Franchini & Alberto Marinelli, 2003. "A Stochastic Model for Representing Drinking Water Demand at Residential Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 17(3), pages 197-222, June.
    • Handle: RePEc:spr:waterr:v:17:y:2003:i:3:p:197-222
    DOI: 10.1023/A:1024100518186
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1024100518186
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1024100518186?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anja Soboll & Michael Elbers & Roland Barthel & Juergen Schmude & Andreas Ernst & Ralf Ziller, 2011. "Integrated regional modelling and scenario development to evaluate future water demand under global change conditions," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 16(4), pages 477-498, April.
    2. Gabriella Balacco & Vincenzo Totaro & Vito Iacobellis & Alessandro Manni & Mauro Spagnoletta & Alberto Ferruccio Piccinni, 2020. "Influence of COVID-19 Spread on Water Drinking Demand: The Case of Puglia Region (Southern Italy)," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    3. Roberto Magini & Manuela Moretti & Maria Antonietta Boniforti & Roberto Guercio, 2023. "A Machine-Learning Approach for Monitoring Water Distribution Networks (WDNs)," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    4. Xiao-Jun Wang & Jian-Yun Zhang & Shamsuddin Shahid & Wei Xie & Chao-Yang Du & Xiao-Chuan Shang & Xu Zhang, 2018. "Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(2), pages 911-924, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:17:y:2003:i:3:p:197-222. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.