Successfully reported this slideshow.
Your SlideShare is downloading. ×

ICT solutions for highly-customized water demand management strategies

Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad

Check these out next

1 of 26 Ad

ICT solutions for highly-customized water demand management strategies

Download to read offline

The recent deployment of smart metering networks is opening new opportunities for advancing residential water demand management strategies (WDMS) that rely on a better understanding of users’ consumption behaviors. Recent applications showed that retrieving information on users’ consumption behaviors, along with their explanatory and/or causal factors, is key to spot potential areas for targeting water saving efforts and to design user-tailored WDMS. In this study, we explore the potential of ICT-based systems in supporting the design and implementation of highly customized WDMS. On one side, the collection of consumption data at high spatial and temporal resolutions requires big data analytics and machine learning techniques to characterize typical consumption profiles of the metered population of users. On the other side, ICT solutions and gamifications can be used as effective means for facilitating both users’ engagement and the collection of socio-psychographic users’ information. This latter allows interpreting and improving the extracted profiles, ultimately supporting the customization of WDMS, such as awareness campaigns or personalized recommendations. Our approach is implemented in the SmartH2O platform and demonstrated in a pilot application in Valencia, Spain. Our results show how the analysis of the smart metered consumption data, combined with the information retrieved from an ICT gamified portal, successfully identifies the typical consumption profiles of the metered users and recommends alternative WDMS targeting the different users’ profiles.

The recent deployment of smart metering networks is opening new opportunities for advancing residential water demand management strategies (WDMS) that rely on a better understanding of users’ consumption behaviors. Recent applications showed that retrieving information on users’ consumption behaviors, along with their explanatory and/or causal factors, is key to spot potential areas for targeting water saving efforts and to design user-tailored WDMS. In this study, we explore the potential of ICT-based systems in supporting the design and implementation of highly customized WDMS. On one side, the collection of consumption data at high spatial and temporal resolutions requires big data analytics and machine learning techniques to characterize typical consumption profiles of the metered population of users. On the other side, ICT solutions and gamifications can be used as effective means for facilitating both users’ engagement and the collection of socio-psychographic users’ information. This latter allows interpreting and improving the extracted profiles, ultimately supporting the customization of WDMS, such as awareness campaigns or personalized recommendations. Our approach is implemented in the SmartH2O platform and demonstrated in a pilot application in Valencia, Spain. Our results show how the analysis of the smart metered consumption data, combined with the information retrieved from an ICT gamified portal, successfully identifies the typical consumption profiles of the metered users and recommends alternative WDMS targeting the different users’ profiles.

Advertisement
Advertisement

More Related Content

Viewers also liked (19)

More from Environmental Intelligence Lab (8)

Advertisement

Recently uploaded (20)

ICT solutions for highly-customized water demand management strategies

  1. 1. ICT solutions for highly-customized water demand management strategies M. Giuliani, A. Cominola, A. Castelletti, P. Fraternali, J. Guardiola, J. Barba, M. Pulido-Velazquez, A.E. Rizzoli
  2. 2. Residential water demand modeling & management resolution depends on the installed meter, the logging time can be shortened without installation of smart meters but simply increasing the traditional reading frequency by the users. However, so far only ad-hoc studies systematically collected and analyzed reconstructing the average flow within the pipe with a resolu- tion of 0.015 L (Kim et al., 2008). Ultrasonic sensors (Mori et al., 2004), which estimate the flow velocity, and then determine the flow rate knowing the pipe section, by measuring the difference in time between ultrasonic beams generated by piezoelectric devices and transmitted within the water flow. The transducers are generally operated in the range 0.5e2 MHz and allow attaining an average resolution around 0.0018 L (e.g., Sanderson and Yeung, 2002). Pressure sensors (Froehlich et al., 2009, 2011), which consist in steel devices, equipped with an analog-digital converter and a micro-controller, continuously sampling pressure with a theo- retical maximum resolution of 2 kHZ. Flow rate is related to the pressure change generated by the opening/close of the water devices valves via Poiseuille's Law. Flow meters (Mayer and DeOreo, 1999), which exploit the water flow to spin either pistons (mechanic flow meters) or magnets (magnetic meters) and correlate the number of revolutions or pulse to the water volume passing through the pipe. Sensing resolution spans between 34.2 and 72 pulses per liter (i.e., 1 pulse every 0.029 and 0.014 L, respectively) associated to a logging frequency in the range of 1e10 s (Kowalski and Marshallsay, 2005; Heinrich, 2007; Willis et al., 2013). So far, only flow meters and pressure sensors have been employed in smart meters applications because ultrasonic sensors are too costly and the use of accelerometers requires an intrusive Fig. 2. Five-years count of the 134 publications reviewed in this study. A. Cominola et al. / Environmental Modelling Software 72 (2015) 198e214200 Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review A. Cominola a , M. Giuliani a , D. Piga b , A. Castelletti a, c, * , A.E. Rizzoli d a Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy b IMT Institute for Advanced Studies Lucca, Lucca, Italy c Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland d Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland a r t i c l e i n f o Article history: Received 2 April 2015 Received in revised form 21 July 2015 Accepted 21 July 2015 Available online xxx Keywords: Smart meter Residential water management Water demand modeling Water conservation a b s t r a c t Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand manage- ment strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, con- strained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world. © 2015 Elsevier Ltd. All rights reserved. 1. Introduction World's urban population is expected to raise from current 54%e66% in 2050 and to further increase as a consequence of the unlikely stabilization of human population by the end of the cen- tury (Gerland et al., 2014). By 2030 the number of mega-cities, namely cities with more than 10 million inhabitants, will grow number of people facing water shortage (McDonald et al., 2011b). In such context, water supply expansion through the construction of new infrastructures might be an option to escape water stress in some situations. Yet, geographical or financial limitations largely restrict such options in most countries (McDonald et al., 2014). Here, acting on the water demand management side through the promotion of cost-effective water-saving technologies, revised Contents lists available at ScienceDirect Environmental Modelling Software journal homepage: www.elsevier.com/locate/envsoft Environmental Modelling Software 72 (2015) 198e214 Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review A. Cominola a , M. Giuliani a , D. Piga b , A. Castelletti a, c, * , A.E. Rizzoli d a Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy b IMT Institute for Advanced Studies Lucca, Lucca, Italy c Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland d Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland a r t i c l e i n f o Article history: Received 2 April 2015 Received in revised form 21 July 2015 Accepted 21 July 2015 Available online xxx Keywords: Smart meter Residential water management Water demand modeling Water conservation a b s t r a c t Over the last two decades, water smart metering programs have been launched in a numb to large cities worldwide to nearly continuously monitor water consumption at the sin level. The availability of data at such very high spatial and temporal resolution advanced characterizing, modeling, and, ultimately, designing user-oriented residential water dem ment strategies. Research to date has been focusing on one or more of these aspects bu integration between the specialized methodologies developed so far. This manuscrip comprehensive review of the literature in this quickly evolving water research doma contributes a general framework for the classification of residential water demand mod which allows revising consolidated approaches, describing emerging trends, and identif future developments. In particular, the future challenges posed by growing population d strained sources of water supply and climate change impacts are expected to require m integrated procedures for effectively supporting residential water demand modeling and m several countries across the world. © 2015 Elsevier Ltd. All ri 1. Introduction World's urban population is expected to raise from current 54%e66% in 2050 and to further increase as a consequence of the unlikely stabilization of human population by the end of the cen- tury (Gerland et al., 2014). By 2030 the number of mega-cities, namely cities with more than 10 million inhabitants, will grow over 40 (UNDESA, 2010). This will boost residential water demand (Cosgrove and Cosgrove, 2012), which nowadays covers a large portion of the public drinking water supply worldwide (e.g., 60e80% in Europe (Collins et al., 2009), 58% in the United States (Kenny et al., 2009)). The concentration of the water demands of thousands or mil- lions of people into small areas will considerably raise the stress on finite supplies of available freshwater (McDonald et al., 2011a). Besides, climate and land use change will further increase the number of people facing water shortage (McDonald et such context, water supply expansion through the co new infrastructures might be an option to escape wa some situations. Yet, geographical or financial limita restrict such options in most countries (McDonald Here, acting on the water demand management side promotion of cost-effective water-saving technolog economic policies, appropriate national and local regu education represents an alternative strategy for secu water supply and reduce water utilities' costs (Gleick In recent years, a variety of water demand manag tegies (WDMS) has been applied (for a review, see Jeffrey, 2006, and references therein). However, the of these WDMS is often context-specific and strongly our understanding of the drivers inducing people to save water (Jorgensen et al., 2009). Models that q describe how water demand is influenced and varies i Environmental Modelling Software journal homepage: www.elsevier.com/locate/envsoft 36% 43% 13% 6% 1% Analysis of 134 studies over the last 25 years Cominola et al. (2015), Environmental Modelling Software
  3. 3. A general procedure Low resolution data High resolution data non-intrusive metering intrusive metering 1. DATA GATHERING Decision tree algorithms Machine learning algorithms 2. WATER END USES CHARACTERIZATION Descriptive models Prescriptive models behavioural modelling multivariate analysis 3. USER MODELLING 4.PERSONALIZED WDMS water consumption at the appliance-level billed data disaggregated water end uses water consumption at the household-level disaggregated water end uses qualitative info on drivers of water consumption quantitative prediction of users’ behaviours High-resolution smart metered water consumption data Models of water user behavior quarterly / half yearly basis readings 1 kilolitre (=1m3) Traditional water meters Smart meters resolution: 72 pulses/L (=72k pulses/m3 ) Data logging resolution: 5-10 s interval Information on time-of-day for consumption Smart water meters 1000 L | quartely readings 0.013 L | 5-10 sec. Cominola et al. (2015), Environmental Modelling Software
  4. 4. A general procedure Low resolution data High resolution data non-intrusive metering intrusive metering 1. DATA GATHERING Decision tree algorithms Machine learning algorithms 2. WATER END USES CHARACTERIZATION Descriptive models Prescriptive models behavioural modelling multivariate analysis 3. USER MODELLING 4.PERSONALIZED WDMS water consumption at the appliance-level billed data disaggregated water end uses water consumption at the household-level disaggregated water end uses qualitative info on drivers of water consumption quantitative prediction of users’ behaviours Models of water user behavior Models of water user behavior Water Utility problem Water Demand Management Strategies: TECHNOLOGICAL (e.g., water efficient devices)
 FINANCIAL (e.g., water price schemes, incentives)
 LEGISLATIVE (e.g., water usage restrictions)
 OPERATION MAINTENANCE (e.g., leak detection)
 EDUCATION (e.g., water awareness campaigns, workshops) Cominola et al. (2015), Environmental Modelling Software
  5. 5. A general procedure Low resolution data High resolution data non-intrusive metering intrusive metering 1. DATA GATHERING Decision tree algorithms Machine learning algorithms 2. WATER END USES CHARACTERIZATION Descriptive models Prescriptive models behavioural modelling multivariate analysis 3. USER MODELLING 4.PERSONALIZED WDMS water consumption at the appliance-level billed data disaggregated water end uses water consumption at the household-level disaggregated water end uses qualitative info on drivers of water consumption quantitative prediction of users’ behaviours Models of water user behavior Models of water user behavior Characterization of the water demand at the household level, possibly as determined by natural and socio-psychographic factors as well as by the users' response to different WDMS. Cominola et al. (2015), Environmental Modelling Software
  6. 6. ICT support
  7. 7. Research questions 1) Can big-data analytics support the identification of consumption profiles?
  8. 8. Research questions 1) Can big-data analytics support the identification of consumption profiles? 2) Can gamification induce behavioral change?
  9. 9. SmartH2O Project in Valencia VALENCIA | ES EMIVASA water supply utility 2 million customers served 490,000watersmart meters currentlyinstalled Developmentplan: 650,000 water smartmeters installedbyend2015 http://www.smarth2o-fp7.eu/
  10. 10. User profiling via hierarchical clustering Analysis of observed water consumption to study historical trends, extract consumption profiles, identify promising areas for demand management options
  11. 11. Classification of average daily demand very high (495 l/d) high (250 l/d) medium (130 l/d) low (30 l/d) DATASET: 11,000 households hourly readings 2 years monitoring period
  12. 12. Weekly patterns DATASET: 11,000 households hourly readings 2 years monitoring period max in week day max in weekend very high (495 l/d) high (250 l/d) medium (130 l/d) low (30 l/d)
  13. 13. Hourly patterns - load shapes DATASET: 11,000 households hourly readings 2 years monitoring period
  14. 14. Most common load shapes DATASET: 11,000 households hourly readings 2 years monitoring period
  15. 15. Most common load shapes DATASET: 11,000 households hourly readings 2 years monitoring period Zero-consumption days
  16. 16. Most common load shapes DATASET: 11,000 households hourly readings 2 years monitoring period Double-peak consumption pattern
  17. 17. Model validation he observed one, with a very small under- and overestimation for medium and high 12. Empirical cumulative density function of daily consumption of EMIVASA users. daily average consumption
  18. 18. Model validation Figure 13. Empirical cumulative density function of daily consumption of EMIVASA users estimated for weekdays (left panel) and weekends (right panel) separately. daily average consumption only week days only weekends
  19. 19. Gamification for behavioral change DropTheQuestion available on
  20. 20. Water consumption reduction - preliminary results historical consumption consumptionaftersH2Otreatment -10%
  21. 21. Behavioral change - preliminary results 0 20 0 20 40 60 80 0 20 40 6080 100 120 0 20 40 60 80 100 20 40 60 80 100 0 0 20 40 60 80 100 120 1400 20 40 60 80 0 20 low (30 l/d) low (30 l/d) medium (130 l/d) medium (130 l/d) high (250 l/d) high (250 l/d) very high (495 l/d) very high (495 l/d) After sH2O treatment History stable behavior decreasing demand increasing demand
  22. 22. Behavioral change - preliminary results 0 20 0 20 40 60 80 0 20 40 6080 100 120 0 20 40 60 80 100 20 40 60 80 100 0 0 20 40 60 80 100 120 1400 20 40 60 80 0 20 low (30 l/d) low (30 l/d) medium (130 l/d) medium (130 l/d) high (250 l/d) high (250 l/d) very high (495 l/d) very high (495 l/d) After sH2O treatment History stable behavior decreasing demand increasing demand
  23. 23. 0 20 0 20 40 60 80 0 20 40 6080 100 120 0 20 40 60 80 100 20 40 60 80 100 0 0 20 40 60 80 100 120 1400 20 40 60 80 0 20 low (30 l/d) low (30 l/d) medium (130 l/d) medium (130 l/d) high (250 l/d) high (250 l/d) very high (495 l/d) very high (495 l/d) After sH2O treatment History stable behavior decreasing demand increasing demand Behavioral change - preliminary results -10%
  24. 24. Take home messages • The large-scale deployment of smart meters is renovating residential water demand management • ICT tools have strong potential for making the most of these new data • Gamification represents a promising option for raising users’ awareness and induce behavioral change
  25. 25. Next Steps Combine the consumption profiles with psychographic information about the users for a better interpretation/ characterization of the extracted profiles. Traditional survey: static snapshot and limited/expensive scalability Online gamified survey: dynamic data collection but limited population’s sample
  26. 26. thank you Matteo Giuliani matteo.giuliani@polimi.it http://giuliani.faculty.polimi.it

×