Developing a stochastic simulation model for the generation of residential water end-use demand time series
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The document discusses the development of a stochastic simulation model for residential water end-use demand as urban populations rise and water demand increases, particularly in developing cities. It reviews existing smart metering programs and highlights the need for integrated approaches to manage water demand effectively amid challenges such as climate change and limited water resources. The paper provides a framework for classifying residential water demand studies and identifies trends and future directions in water management strategies.
![NRM
Generation of end-use water demand
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
753,076 water-use events
over 4,036 monitoring days
across 9 US cities
Source: DeOreo [2011]](https://image.slidesharecdn.com/iemss2016generatorgiuliani-170127114729/85/Developing-a-stochastic-simulation-model-for-the-generation-of-residential-water-end-use-demand-time-series-9-320.jpg)
![NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
• building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
• allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
• User-friendly interface
• Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
• Trained on high-resolutions (1 second) consumption data from 9 cities across USA
• Performance validated with a two-sample Kolmogorov-Smirnov test
• Flexible for synthetic generation at multi-scale resolutions.
user’s input end-use statistics extraction end-use traces generation
• house size
• time sampling
resolution
• device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
753,076 water-use events
over 4,036 monitoring days
across 9 US cities
Source: DeOreo [2011]](https://image.slidesharecdn.com/iemss2016generatorgiuliani-170127114729/85/Developing-a-stochastic-simulation-model-for-the-generation-of-residential-water-end-use-demand-time-series-10-320.jpg)
Developing a stochastic simulation model for the generation of residential water end-use demand time series
- 1. Developing a stochastic simulation model for the generation of residential water end-use demand time series A. Cominola1, M. Giuliani1, A. Castelletti1, A.M. Abdallah2, D.E. Rosenberg2 1 Dept. Electronics, Information, and Bioengineering - Hydroinformatics Lab, Politecnico di Milano 2 Dept. of Civil and Environmental Engineering, Utah State University
- 2. NRM Urban population is growing US 246.2 Urban population in millions 81% Urban percentage Mexico 84.392 77% Colombia 34.3 73% Brazil 162.6 85% Argentina 35.6 90% Ukraine 30.9 68% Russia 103.6 73% China 559.2Urban population in millions 42%Urban percentage Turkey 51.1 68% India 329.3 29% Bangladesh 38.2 26% Philippines 55.0 64% Indonesia 114.1 50% S Korea 39.0 81% Japan 84.7 66% Egypt 33.1 43% S Africa 28.6 60% Canada 26.3 Venezuela 26.0 Poland 23.9 Thailand 21.5 Australia 18.3 Netherlands 13.3 Peru 21.0 Saudi Arabia 20.9 Iraq 20.3 Vietnam 23.3 DR Congo 20.2 Algeria 22.0Morocco 19.4 Malaysia 18.1 Burma 16.5 Sudan 16.3 Chile 14.6 N Korea 14.1 Ethiopia 13.0 Uzbekistan 10.1 Tanzania 9.9 Romania 11.6 Ghana 11.3 Syria 10.2 Belgium 10.2 80% 94% 62% 33% 89% 81% 73% 81% 67% 27% 33% 65% 60% 69% 32% 43% 88% 62% 16% 37% 25% 54% 49% 51% 97% Nigeria 68.6 50% UK 54.0 90% France 46.9 77% Spain 33.6 77% Italy 39.6 68% Germany 62.0 75% Iran 48.4 68% Pakistan 59.3 36% Cameroon Angola Ecuador Ivory Coast Kazakh- stan Cuba Afghan- istan Sweden Kenya Czech Republic 9.5 9.3 8.7 8.6 8.6 8.5 7.8 7.6 7.6 7.4 Mozam- bique Hong Kong Belarus Tunisia Hungary Greece Israel Guate- mala Portugal Yemen Dominican Republic Bolivia Serbia & Mont Switzer- land Austria Bulgaria Mada- gascar Libya Senegal Jordan Zimbabwe Nepal Denmark Mali Azerbaijan Singapore El Salvador Zambia Uganda Puerto Rico Paraguay UAE Benin Norway New Zealand Honduras Haiti Nicaragua Guinea Finland Uruguay Lebanon Somalia Sri Lanka Cambodia Slovakia Costa Rica Palestine Kuwait Togo Chad Burkina Ireland Croatia Congo Niger Sierra Leone Malawi Panama Turkmenistan Georgia Lithuania Liberia Moldova Rwanda Kyrgyzstan Oman Armenia Bosnia Tajikistan CAR Melanesia Latvia Mongolia Albania Jamaica Macedonia Mauritania Laos Gabon Botswana Slovenia Eritrea Estonia Gambia Burundi Papua New Guinea Namibia Mauritius Guinea-Bissau Lesotho E Timor Bhutan Swaziland Trinidad & Tobago The earth reaches a momentous milestone: by next year, for the first time in history, more than half its population will be living in cities. Those 3.3 billion people are expected to grow to 5 billion by 2030 — this unique map of the world shows where those people live now At the beginning of the 20th century, the world's urban population was only 220 million, mainly in the west By 2030, the towns and cities of the developing world will make up 80% of urban humanity The new urban world Urban growth, 2005—2010 Predominantly urban 75% or over Predominantly urban 50—74% Predominantly rural 25—49% urban Predominantly rural 0—24% urban Cities over 10 million people (greater urban area) Key Tokyo 33.4 Osaka 16.6 Seoul 23.2 Manila 15.4 Jakarta 14.9 Dacca 13.8 Bombay 21.3 Delhi 21.1 Calcutta 15.5 Karachi 14.8 Shanghai 17.3 Canton 14.5 Beijing 12.7 Moscow 13.4 Tehran 12.1 Cairo 15.9 Istanbul 11.7 London 12.0 Lagos 10.0 Mexico City 22.1 New York 21.8 Sao Paulo 20.4 LA 17.9 Rio de Janeiro 12.2 Buenos Aires 13.5 3,307,950,000The world’s urban population — from a total of 6,615.9 million SOURCE: UNFPA GRAPHIC: PAUL SCRUTONAfrica Asia Oceania Europe 0.1% Eastern Europe -0.4% Arab States Latin America & Caribbean North America 3.2% 2.4% 1.3% 2.8% 1.7% 1.3%
- 3. NRM Residential water demand 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 data at daily resolution (e.g., Olmstead et al., 2007; Wong et al., 2010) and few water companies (e.g., Water Corporation in West- ern Australia and Thames Water in London) started increasing their reading frequency by direct involvement of their customers, who 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 calibration phase with the placement of multiple meters distrib- uted on the pipe network for each single device of interest (Kim et al., 2008). It is worth noting that the “smartness” of these sen- 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 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 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 economic policies, appropriate national and local regulations, and education represents an alternative strategy for securing reliable water supply and reduce water utilities' costs (Gleick et al., 2003). In recent years, a variety of water demand management stra- tegies (WDMS) has been applied (for a review, see Inman and Jeffrey, 2006, and references therein). However, the effectiveness of these WDMS is often context-specific and strongly depends on our understanding of the drivers inducing people to consume or save water (Jorgensen et al., 2009). Models that quantitatively describe how water demand is influenced and varies in relation to exogenous uncontrolled drivers (e.g., seasonality, climatic condi- tions) and demand management actions (e.g., water restrictions, pricing schemes, education campaigns) are essential to explore water users' response to alternative WDMS, ultimately supporting * Corresponding author. Department of Electronics, Information, and Bioengi- neering, Politecnico di Milano, Milan, Italy. E-mail address: andrea.castelletti@polimi.it (A. Castelletti). Contents lists available at ScienceDirect Environmental Modelling Software journal homepage: www.elsevier.com/locate/envsoft http://dx.doi.org/10.1016/j.envsoft.2015.07.012 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 numbe to large cities worldwide to nearly continuously monitor water consumption at the singl level. The availability of data at such very high spatial and temporal resolution advanced t characterizing, modeling, and, ultimately, designing user-oriented residential water dema ment strategies. Research to date has been focusing on one or more of these aspects but integration between the specialized methodologies developed so far. This manuscript comprehensive review of the literature in this quickly evolving water research domain contributes a general framework for the classification of residential water demand mode which allows revising consolidated approaches, describing emerging trends, and identifyi future developments. In particular, the future challenges posed by growing population de strained sources of water supply and climate change impacts are expected to require mo integrated procedures for effectively supporting residential water demand modeling and ma several countries across the world. © 2015 Elsevier Ltd. All righ 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 al such context, water supply expansion through the con new infrastructures might be an option to escape wat some situations. Yet, geographical or financial limitati restrict such options in most countries (McDonald et Here, acting on the water demand management side t promotion of cost-effective water-saving technologi economic policies, appropriate national and local regul education represents an alternative strategy for secur water supply and reduce water utilities' costs (Gleick e In recent years, a variety of water demand manage tegies (WDMS) has been applied (for a review, see Jeffrey, 2006, and references therein). However, the ef of these WDMS is often context-specific and strongly d our understanding of the drivers inducing people to c save water (Jorgensen et al., 2009). Models that qu describe how water demand is influenced and varies in exogenous uncontrolled drivers (e.g., seasonality, clim tions) and demand management actions (e.g., water r pricing schemes, education campaigns) are essential water users' response to alternative WDMS, ultimately * Corresponding author. Department of Electronics, Information, and Bioengi- neering, Politecnico di Milano, Milan, Italy. E-mail address: andrea.castelletti@polimi.it (A. Castelletti). Contents lists available at ScienceDirect Environmental Modelling Software journal homepage: www.elsevier.com/locate/envsoft http://dx.doi.org/10.1016/j.envsoft.2015.07.012 1364-8152/© 2015 Elsevier Ltd. All rights reserved. Environmental Modelling Software 72 (2015) 198e214 36% 43% 13% 6% 1% Analysis of 134 studies over the last 25 years
- 4. NRM Residential water demand 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 data at daily resolution (e.g., Olmstead et al., 2007; Wong et al., 2010) and few water companies (e.g., Water Corporation in West- ern Australia and Thames Water in London) started increasing their reading frequency by direct involvement of their customers, who 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 calibration phase with the placement of multiple meters distrib- uted on the pipe network for each single device of interest (Kim et al., 2008). It is worth noting that the “smartness” of these sen- 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 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 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 economic policies, appropriate national and local regulations, and education represents an alternative strategy for securing reliable water supply and reduce water utilities' costs (Gleick et al., 2003). In recent years, a variety of water demand management stra- tegies (WDMS) has been applied (for a review, see Inman and Jeffrey, 2006, and references therein). However, the effectiveness of these WDMS is often context-specific and strongly depends on our understanding of the drivers inducing people to consume or save water (Jorgensen et al., 2009). Models that quantitatively describe how water demand is influenced and varies in relation to exogenous uncontrolled drivers (e.g., seasonality, climatic condi- tions) and demand management actions (e.g., water restrictions, pricing schemes, education campaigns) are essential to explore water users' response to alternative WDMS, ultimately supporting * Corresponding author. Department of Electronics, Information, and Bioengi- neering, Politecnico di Milano, Milan, Italy. E-mail address: andrea.castelletti@polimi.it (A. Castelletti). Contents lists available at ScienceDirect Environmental Modelling Software journal homepage: www.elsevier.com/locate/envsoft http://dx.doi.org/10.1016/j.envsoft.2015.07.012 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 numbe to large cities worldwide to nearly continuously monitor water consumption at the singl level. The availability of data at such very high spatial and temporal resolution advanced t characterizing, modeling, and, ultimately, designing user-oriented residential water dema ment strategies. Research to date has been focusing on one or more of these aspects but integration between the specialized methodologies developed so far. This manuscript comprehensive review of the literature in this quickly evolving water research domain contributes a general framework for the classification of residential water demand mode which allows revising consolidated approaches, describing emerging trends, and identifyi future developments. In particular, the future challenges posed by growing population de strained sources of water supply and climate change impacts are expected to require mo integrated procedures for effectively supporting residential water demand modeling and ma several countries across the world. © 2015 Elsevier Ltd. All righ 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 al such context, water supply expansion through the con new infrastructures might be an option to escape wat some situations. Yet, geographical or financial limitati restrict such options in most countries (McDonald et Here, acting on the water demand management side t promotion of cost-effective water-saving technologi economic policies, appropriate national and local regul education represents an alternative strategy for secur water supply and reduce water utilities' costs (Gleick e In recent years, a variety of water demand manage tegies (WDMS) has been applied (for a review, see Jeffrey, 2006, and references therein). However, the ef of these WDMS is often context-specific and strongly d our understanding of the drivers inducing people to c save water (Jorgensen et al., 2009). Models that qu describe how water demand is influenced and varies in exogenous uncontrolled drivers (e.g., seasonality, clim tions) and demand management actions (e.g., water r pricing schemes, education campaigns) are essential water users' response to alternative WDMS, ultimately * Corresponding author. Department of Electronics, Information, and Bioengi- neering, Politecnico di Milano, Milan, Italy. E-mail address: andrea.castelletti@polimi.it (A. Castelletti). Contents lists available at ScienceDirect Environmental Modelling Software journal homepage: www.elsevier.com/locate/envsoft http://dx.doi.org/10.1016/j.envsoft.2015.07.012 1364-8152/© 2015 Elsevier Ltd. All rights reserved. Environmental Modelling Software 72 (2015) 198e214 36% 43% 13% 6% 1% Analysis of 134 studies over the last 25 years first smart meters deployment
- 5. NRM Traditional water meters monthly to yearly readings Oct Nov Dec Waterconsumptionm3 ? 1 cubic meter
- 6. NRM Oct Nov Dec Waterconsumptionm3 ? Smart water meters Data logging at 5-10 sec72 pulses/L
- 7. Smart meters deployment: OPPORTUNITIES • End-use characterization • Advanced management strategies • Projections of water demand
- 8. Smart meters deployment: CHALLENGES • Deployment and maintenance costs • Big data collection, processing and analysis • Intrusiveness and privacy
- 9. NRM Generation of end-use water demand 5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet 753,076 water-use events over 4,036 monitoring days across 9 US cities Source: DeOreo [2011]
- 10. NRM Generation of end-use water demand DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu • building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com testing; • allowing for the generation of end-use patterns under different demographic and technological scenarios. DEVELOPMENT PLAN • User-friendly interface • Web portal to contribute with new datasets from different case studies CURRENT FEATURES • Trained on high-resolutions (1 second) consumption data from 9 cities across USA • Performance validated with a two-sample Kolmogorov-Smirnov test • Flexible for synthetic generation at multi-scale resolutions. user’s input end-use statistics extraction end-use traces generation • house size • time sampling resolution • device presence usage duration usage volume time-of-use frequency of use typical pattern (signature) 34 % Andrea Cominol andrea.cominola@polimi @smartH2Oproje #SmartH2 time water consumption 5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet 753,076 water-use events over 4,036 monitoring days across 9 US cities Source: DeOreo [2011]
- 11. NRM Generation of end-use water demand DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu • building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com testing; • allowing for the generation of end-use patterns under different demographic and technological scenarios. DEVELOPMENT PLAN • User-friendly interface • Web portal to contribute with new datasets from different case studies CURRENT FEATURES • Trained on high-resolutions (1 second) consumption data from 9 cities across USA • Performance validated with a two-sample Kolmogorov-Smirnov test • Flexible for synthetic generation at multi-scale resolutions. user’s input end-use statistics extraction end-use traces generation • house size • time sampling resolution • device presence usage duration usage volume time-of-use frequency of use typical pattern (signature) 34 % Andrea Cominol andrea.cominola@polimi @smartH2Oproje #SmartH2 time water consumption 5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet Duration Volume Time of use # daily uses
- 12. NRM Generation of end-use water demand DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu • building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com testing; • allowing for the generation of end-use patterns under different demographic and technological scenarios. DEVELOPMENT PLAN • User-friendly interface • Web portal to contribute with new datasets from different case studies CURRENT FEATURES • Trained on high-resolutions (1 second) consumption data from 9 cities across USA • Performance validated with a two-sample Kolmogorov-Smirnov test • Flexible for synthetic generation at multi-scale resolutions. user’s input end-use statistics extraction end-use traces generation • house size • time sampling resolution • device presence usage duration usage volume time-of-use frequency of use typical pattern (signature) 34 % Andrea Cominol andrea.cominola@polimi @smartH2Oproje #SmartH2 time water consumption 5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet Duration Volume Time of use # daily uses Typical pattern - signature
- 13. NRM Generation of end-use water demand DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu • building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com testing; • allowing for the generation of end-use patterns under different demographic and technological scenarios. DEVELOPMENT PLAN • User-friendly interface • Web portal to contribute with new datasets from different case studies CURRENT FEATURES • Trained on high-resolutions (1 second) consumption data from 9 cities across USA • Performance validated with a two-sample Kolmogorov-Smirnov test • Flexible for synthetic generation at multi-scale resolutions. user’s input end-use statistics extraction end-use traces generation • house size • time sampling resolution • device presence usage duration usage volume time-of-use frequency of use typical pattern (signature) 34 % Andrea Cominol andrea.cominola@polimi @smartH2Oproje #SmartH2 time water consumption 5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
- 14. NRM Generation of end-use water demand DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu • building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com testing; • allowing for the generation of end-use patterns under different demographic and technological scenarios. DEVELOPMENT PLAN • User-friendly interface • Web portal to contribute with new datasets from different case studies CURRENT FEATURES • Trained on high-resolutions (1 second) consumption data from 9 cities across USA • Performance validated with a two-sample Kolmogorov-Smirnov test • Flexible for synthetic generation at multi-scale resolutions. user’s input end-use statistics extraction end-use traces generation • house size • time sampling resolution • device presence usage duration usage volume time-of-use frequency of use typical pattern (signature) 34 % Andrea Cominol andrea.cominola@polimi @smartH2Oproje #SmartH2 time water consumption 5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
- 15. NRM Generation of end-use water demand DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu • building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com testing; • allowing for the generation of end-use patterns under different demographic and technological scenarios. DEVELOPMENT PLAN • User-friendly interface • Web portal to contribute with new datasets from different case studies CURRENT FEATURES • Trained on high-resolutions (1 second) consumption data from 9 cities across USA • Performance validated with a two-sample Kolmogorov-Smirnov test • Flexible for synthetic generation at multi-scale resolutions. user’s input end-use statistics extraction end-use traces generation • house size • time sampling resolution • device presence usage duration usage volume time-of-use frequency of use typical pattern (signature) 34 % Andrea Cominol andrea.cominola@polimi @smartH2Oproje #SmartH2 time water consumption 5 Devices: toilet, clothes washer, showers, dishwasher, faucet
- 19. NRM Projections of water demand: # inhabitants
- 20. NRM Projections of water demand: device efficiency - 40% - 50% - 2%
- 21. NRM Conclusions This preliminary results show great potential for supporting smart meters deployments and end-use water demand analysis. Outlook: 1. Update of the database with new report by Aquacraft Inc. 2. Web service for community development 3. Creation of open database across countries