Smart meters for enhanced water supply network modelling and infrastructure planning |
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| Institution: | 1. Griffith School of Engineering, Griffith University, Gold Coast Campus, QLD 4222, Australia;2. Centre for Infrastructure Engineering & Management, Griffith University, Gold Coast Campus 4222, Australia;3. CSIRO Land and Water, Highett, VIC 3190, Australia;4. Smart Water Research Centre, Griffith University, Southport, QLD 4222, Australia;1. School of Engineering, Griffith University, Gold Coast Campus 4222, Australia;2. Department of Industrial Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Saudi Arabia;3. Centre for Infrastructure Engineering & Management, Griffith University, Gold Coast Campus 4222, Australia;4. Smart Water Research Centre, Griffith University, Gold Coast Campus 4222, Australia;1. Institute of Urban Water Management and Landscape Water Engineering, Graz University of Technology, Stremayrgasse 10/I, 8010 Graz, Austria;2. Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Inffeldgasse 23/II, 8010 Graz, Austria;1. School of Engineering and Built Environment, Griffith University, Queensland, Australia;2. Cities Research Institute, Griffith University, Queensland, Australia;3. Griffith Climate Change Response Program, Griffith University, Queensland, Australia;4. City of Gold Coast, Queensland, Australia;5. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy;6. Institute for Sustainable Futures, University Technology Sydney, New South Wales, Australia;7. Faculty of Engineering and IT, University of Technology Sydney, New South Wales, Australia;8. School of Chemical Engineering, The University of Queensland, Queensland, Australia;9. Centre for Water Systems, University of Exeter, United Kingdom;10. EPSRC Centre for Doctoral Training in Water Informatics: Science and Engineering (WISE CDT), University of Exeter, United Kingdom;11. School of Civil Engineering, National Technical University of Athens, Athens, Attiki, Greece;1. ETH Zürich, Centre for Energy Policy and Economics, Switzerland;2. Università della Svizzera Italiana, Switzerland;3. Universidad de La Laguna, Spain;1. Department of Civil, Design, Structural and Environmental Engineering, via Roma 29, 81031, Aversa, Italy;2. Urban Hydraulics Department, Mexican Institute of Water Technology, Jiutepec, Mor, Mexico |
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| 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. |
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| Keywords: | Diurnal patterns Smart meters Peaking factors Water demand profiles Water supply network modelling Water end use |
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