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Smart meters for enhanced water supply network modelling and infrastructure planning

Author

Listed:
  • Gurung, Thulo Ram
  • Stewart, Rodney A.
  • Sharma, Ashok K.
  • Beal, Cara D.

Abstract

To design water distribution network infrastructure, water utilities formulate daily demand profiles and peaking factors. However, traditional methods of developing such profiles and peaking factors, necessary to carry out water distribution network modelling, are often founded on a number of assumptions on how top-down bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters allows for a new novel methodology for using the continuous ‘big data’ generated by these meter fleets to create evidence-based water demand curves suitable for use in network models. To demonstrate the application of the developed method, high resolution water consumption data from households fitted with smart water meters were collected from the South East Queensland and Hervey Bay regions in Australia. Average day (AD), peak day (PD) and mean day maximum month (MDMM) demand curves, often used in water supply network modelling, were developed from the herein created methodology using both individual end-use level and hourly demand patterns from the smart meters. The resulting modelled water demand patterns for AD, PD and MDMM had morning and evening peaks occurring earlier and lower main peaks (AD: 12%; PD: 20%; MDMM: 33%) than the currently used demand profiles of the regions’ water utility. The paper concludes with a discussion on the implications of widespread smart water metering systems for enhanced water distribution infrastructure planning and management as well as the benefits to customers.

Suggested Citation

  • Gurung, Thulo Ram & Stewart, Rodney A. & Sharma, Ashok K. & Beal, Cara D., 2014. "Smart meters for enhanced water supply network modelling and infrastructure planning," Resources, Conservation & Recycling, Elsevier, vol. 90(C), pages 34-50.
    • Handle: RePEc:eee:recore:v:90:y:2014:i:c:p:34-50
    DOI: 10.1016/j.resconrec.2014.06.005
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    References listed on IDEAS

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