An Approach to Disaggregating Total Household Water Consumption into Major End-Uses

Fontdecaba, Sara; Sánchez-Espigares, José A.; Almagro, Lluís Marco; Tort‐Martorell, Xavier; Cabrespina, Francesc; Zubelzu, Jordi

doi:10.1007/s11269-013-0281-8

Cited by 17 publications

(11 citation statements)

References 4 publications

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“…Nevertheless, this situation is fast changing with greater production of smart metering technology as well as the rapidly developing hydroinformatic applications being realised (e.g. Britton et al 2013;Fontdecaba et al, 2013;Nguyen et al, 2014).…”

Section: Smart Water Meters For Assisting In Network Augmentationsmentioning

confidence: 99%

Smart meter enabled informatics for economically efficient diversified water supply infrastructure planning

Gurung

Stewart

Beal

et al. 2016

Journal of Cleaner Production

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Water efficiency measures and alternative supply sources alleviate peak water demand on urban water supply networks. Consequently, they also provide benefits to water service providers, in terms of augmentation deferrals and reduced sized infrastructure. However, while these benefits are acknowledged in the literature, they have not been thoroughly investigated and quantified. This paper empirically demonstrates how the installation of different potable water saving measures would affect the design of urban water supply networks. Peak day water demand profiles were developed for the baseline scenario, which represented the typical building code mandated for new dwellings constructed in Queensland, Australia, and for households fitted with water saving measures. The core novel feature of this study relates to the use of an innovative bottom-up approach to the development of demand profiles based on smart meters enabling comprehensive water end use datasets (i.e. demand in shower, tap, etc.) to be obtained. Hydraulic model runs were conducted for various water savings scenarios across different planning horizons to determine the scheduling of augmentations in a water supply study area. The results of the model runs showed deferred and eliminated augmentations, as well as reductions in infrastructure sizing for the water savings scenarios compared to the baseline scenario. Financial analysis (i.e. NPV) on trunk main augmentation requirements over 50 year asset life cycles indicated that savings of between $1,574,289 (11.4%) and $7,030,796 (51%) could be achieved by incorporating water efficiency and potable source substitution measures in new infill developments in the study region.

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Section: Smart Water Meters For Assisting In Network Augmentationsmentioning

confidence: 99%

Smart meter enabled informatics for economically efficient diversified water supply infrastructure planning

Gurung

Stewart

Beal

et al. 2016

Journal of Cleaner Production

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“…Flow meters are the most commonly-used sensing modality [ 34 , 35 , 36 ], as they can be fitted outside of the premises of a customer and are, thus, the least intrusive instrumentation option from an occupier’s perspective. They directly measure the volume of utilised water usage activities, which forms the basis of customer billing.…”

Section: Approaches For Contextualizing Domestic Water Usementioning

confidence: 99%

Contextualising Water Use in Residential Settings: A Survey of Non-Intrusive Techniques and Approaches

Carboni

Gluhak

McCann

et al. 2016

Sensors

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Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included.

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“…Having large fleets of such very high resolution smart meters producing water end use datasets is presently not feasible due to the requirement for manual flow trace analysis, which is a resource intensive process. However, promising research by Nguyen et al (2013aNguyen et al ( , 2013bNguyen et al ( and 2014 and Fontdecaba et al (2013) to automate this process, makes this possible in the near future. Given that high resolution end use data was available for this study, it has been adopted here to demonstrate the full application of the developed methodology described in Fig.…”

Section: Tasksmentioning

confidence: 99%

“…This is set to change in the future with technology becoming mass produced and considerably less expensive. Moreover, promising research on automating end use classifications has resulted in devices being correctly identified at more than 70% of the time (Nguyen et al, 2013a(Nguyen et al, , 2013b(Nguyen et al, , 2014Fontdecaba et al, 2013), with further research proposed to increase the accuracy of end-use analysis.…”

Section: Smart Water Meters' Limitations and Capabilitiesmentioning

confidence: 99%

Smart meters for enhanced water supply network modelling and infrastructure planning

Gurung

Stewart

Sharma

et al. 2014

Resources, Conservation and Recycling

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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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An Approach to Disaggregating Total Household Water Consumption into Major End-Uses

Cited by 17 publications

References 4 publications

Smart meter enabled informatics for economically efficient diversified water supply infrastructure planning

Smart meter enabled informatics for economically efficient diversified water supply infrastructure planning

Contextualising Water Use in Residential Settings: A Survey of Non-Intrusive Techniques and Approaches

Smart meters for enhanced water supply network modelling and infrastructure planning

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