Green Smart Technology for Water (GST4Water): Water Loss Identification at User Level by Using Smart Metering Systems
Abstract
:1. Introduction
2. The Monitoring and Processing System
2.1. The Monitoring System
2.2. The Processing System
2.3. Algorithm for The Automatic Identification of Water Losses Inside User Households
3. Case Study
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Boyle, T.; Giurco, D.; Mukheibir, P.; Liu, A.; Moy, C.; White, S.; Stewart, R. Intelligent Metering for Urban Water: A Review. Water 2013, 5, 1052–1081. [Google Scholar] [CrossRef] [Green Version]
- Willis, R.M.; Stewart, R.A.; Panuwatwanich, K.; Williams, P.R.; Hollingsworth, A.L. Quantifying the influence of environmental and water conservation attitudes on household end use water consumption. J. Environ. Manag. 2011, 92, 1996–2009. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Buchberger, S.G.; Nadimpalli, G. Leak Estimation in Water Distribution Systems by Statistical Analysis of Flow Readings. J. Water Resour. Plann. Manag. 2004, 130, 321–329. [Google Scholar] [CrossRef]
- Cominola, A.; Giuliani, M.; Piga, D.; Castelletti, A.; Rizzoli, A.E. Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review. Environ. Model. Softw. 2015, 72, 198–214. [Google Scholar] [CrossRef] [Green Version]
- Muthukumaran, S.; Baskaran, K.; Sexton, N. Quantification of potable water savings by residential water conservation and reuse—A case study. Resour. Conserv. Recycl. 2011, 55, 945–952. [Google Scholar] [CrossRef]
- GST4Water, Project Website. Available online: https://www.gst4water.it/ (accessed on 25 January 2019).
- Luciani, C.; Casellato, F.; Alvisi, S.; Franchini, M. From Water Consumption Smart Metering to Leakage Characterization at District and User Level: The GST4Water Project. In Proceedings of the 3rd EWaS International Conference on Insights on the Water-Energy-Food Nexus, Lefkada Island, Greece, 27–30 June 2018; Volume 2. [Google Scholar] [CrossRef]
- Cipolla, S.S.; Altobelli, M.; Maglionico, M. Decentralized water management: Rainwater harvesting, greywater reuse and green roofs within the GST4Water project. In Proceedings of the 3rd EWaS International Conference on Insights on the Water-Energy-Food Nexus, Lefkada Island, Greece, 27–30 June 2018; Volume 2. [Google Scholar] [CrossRef]
- Di Fusco, E.; Lenci, A.; Liserra, T.; Ciriello, V.; Di Federico, V. Sustainability assessment of urban water use from building to urban scale in the GST4Water project. In Proceedings of the 3rd EWaS International Conference on “Insights on the Water-Energy-Food Nexus, Lefkada Island, Greece, 27–30 June 2018; Volume 2. [Google Scholar] [CrossRef]
- Puust, R.; Kapelan, Z.; Savić, D.A.; Koppel, T. A review of methods for leakage management in pipe networks. Urban Water J. 2010, 7, 25–45. [Google Scholar] [CrossRef]
- Farley, M.; Trow, S. Losses in Water Distribution Networks—A Practitioner’s Guide to Assessment, Monitoring and Control; IWA Publishing: London UK, 2003; pp. 146–149. ISBN 13 9781900222112. [Google Scholar]
- IWA, Water Loss Task Force. Best Practice Performance Indicators for Non-Revenue Water and Water Loss Components: A Practical Approach; International Water Association: London, UK, 2005. [Google Scholar]
- Britton, T.; Cole, G.; Stewart, R.; Wiskar, D. Remote diagnosis of leakage in residential households. Water (Australian Water Association) 2008, 35, 56–60. [Google Scholar]
- Britton, T.C.; Stewart, R.A.; O’Halloran, K.R. Smart metering: Enabler for rapid and effective post meter leakage identification and water loss management. J. Clean. Prod. 2013, 54, 166–176. [Google Scholar] [CrossRef]
- Article on a Water Leakage Inside a User in Ferrara, La Nuova Ferrara Website. Available online: https://lanuovaferrara.gelocal.it/ferrara/cronaca/2018/02/20/news/il-tubo-rotto-e-un-salasso-bolletta-da-16mila-euro-1.16504782 (accessed on 13 February 2019).
- Article on a Water Leakage Inside a User in Province of Venice, Venezia Today Website. Available online: http://www.veneziatoday.it/cronaca/bollette-acquaperdite-occulte.html (accessed on 13 February 2019).
- Stewart, R.A.; Nguyen, K.; Beal, C.; Zhang, H.; Sahin, O.; Bertone, E.; Vieira, A.S.; Castelletti, A.; Cominola, A.; Giuliani, M.; et al. Integrated intelligent water-energy metering systems and informatics: Visioning a digital multi-utility service provider. Environ. Model. Softw. 2018, 105, 94–117. [Google Scholar] [CrossRef]
- Gurung, T.R.; Stewart, R.A.; Sharma, A.K.; Beal, C.D. Smart meters for enhanced water supply network modelling and infrastructure planning. Resour. Conserv. Recycl. 2014, 90, 34–50. [Google Scholar] [CrossRef] [Green Version]
- Boulos, P.F.; Wiley, A.N. Can we make water systems smarter? Opflow 2013, 39, 20–22. [Google Scholar] [CrossRef]
- Froehlich, J.; Findlater, L.; Ostergren, M.; Ramanathan, S.; Peterson, J.; Wragg, I.; Larson, E.; Fu, F.; Bai, M.; Patel, S.; et al. The design and evaluation of prototype eco-feedback displays for fixture-level water usage data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Austin, TX, USA, 5–10 May 2012; ACM: New York, NY, USA, 2012; pp. 2367–2376. [Google Scholar] [CrossRef]
- Sonderlund, A.L.; Smith, J.R.; Hutton, C.; Kapelan, Z. Using smart meters for household water consumption feedback: Knowns and unknowns. Procedia Eng. 2014, 89, 990–997. [Google Scholar] [CrossRef]
- Geller, E.S. The challenge of increasing proenvironment behavior. In Handbook of Environmental Psychology; Wiley: New York, NY, USA, 2002; Chapter 34; pp. 525–540. [Google Scholar]
- Fielding, K.S.; Spinks, A.; Russell, S.; McCrea, R.; Stewart, R.; Gardner, J. An experimental test of voluntary strategies to promote urban water demand management. J. Environ. Manag. 2013, 114, 343–351. [Google Scholar] [CrossRef] [PubMed]
- Stewart, R.A.; Willis, R.; Giurco, D.P.; Panuwatwanich, K.; Capati, G. Web based knowledge management system: Linking smart metering to the future of urban water planning. Aust. Plan. 2010, 47, 66–74. [Google Scholar] [CrossRef]
- DeOreo, W.B.; Heaney, J.P.; Mayer, P.W. Flow trace analysis to assess water use. AWWA 1996, 88, 79–90. [Google Scholar] [CrossRef]
- Linux. Available online: https://www.linux.org/ (accessed on 14 February 2019).
- Supervisor. Available online: https://github.com/Supervisor/supervisor; http://supervisord.org/ (accessed on 14 February 2019).
- Sakis3g. Available online: https://github.com/trixarian/sakis3g-source (accessed on 14 February 2019).
- Wang, G.; Hao, J.; Ma, J.; Huang, L. A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 2010, 37, 6225–6232. [Google Scholar] [CrossRef]
- Farah, E.; Shahrour, I. Leakage Detection Using Smart Water System: Combination of Water Balance and Automated Minimum Night Flow. Water Resour. Manag. 2017, 31, 4821–4833. [Google Scholar] [CrossRef]
- Davis, J.; Goadrich, M. The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, 25–29 June 2006; ACM: New York, NY, USA, 2016; pp. 233–240. [Google Scholar]
- Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006, 27, 861–874. [Google Scholar] [CrossRef]
Benchmark | |||
---|---|---|---|
Presence of Water Loss | Absence of Water Loss | ||
Algorithm | Presence of water loss | TP | FP |
Absence of water loss | FN | TN |
(a) | (b) | ||||||
---|---|---|---|---|---|---|---|
Benchmark | Benchmark | ||||||
Presence of Water Loss | Absence of Water Loss | Presence of Water Loss | Absence of Water Loss | ||||
A2-5 | Presence of water loss | TP = 18,896 (18.4%) | FP = 5990 (5.8%) | A0-24 | Presence of water loss | TP = 18,211 (17.7%) | FP = 718 (0.7%) |
Absence of water loss | FN = 199 (0.2%) | TN = 77,701 (75.6%) | Absence of water loss | FN = 880 (0.9%) | TN = 82,965 (80.7%) | ||
TP + FP + FN + TN = 102,786 | TP + FP + FN + TN = 10,2774 | ||||||
TP + FN = 19,095 | TP + FN = 19,091 | ||||||
TN + FP = 83,691 | TN + FP = 83,683 | ||||||
TP + FP = 24,886 | TP + FP = 18,929 | ||||||
TN + FN = 77,900 | TN + FN = 83,845 |
Metrics- | A2-5 | A0-24 | A0-48 |
---|---|---|---|
Accuracy | 0.94 | 0.98 | 0.97 |
Recall | 0.99 | 0.95 | 0.88 |
Specificity | 0.93 | 0.99 | 0.99 |
Precision | 0.73 | 0.96 | 0.99 |
F1 | 0.86 | 0.96 | 0.93 |
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Luciani, C.; Casellato, F.; Alvisi, S.; Franchini, M. Green Smart Technology for Water (GST4Water): Water Loss Identification at User Level by Using Smart Metering Systems. Water 2019, 11, 405. https://doi.org/10.3390/w11030405
Luciani C, Casellato F, Alvisi S, Franchini M. Green Smart Technology for Water (GST4Water): Water Loss Identification at User Level by Using Smart Metering Systems. Water. 2019; 11(3):405. https://doi.org/10.3390/w11030405
Chicago/Turabian StyleLuciani, Chiara, Francesco Casellato, Stefano Alvisi, and Marco Franchini. 2019. "Green Smart Technology for Water (GST4Water): Water Loss Identification at User Level by Using Smart Metering Systems" Water 11, no. 3: 405. https://doi.org/10.3390/w11030405