Advancing the Cyberinfrastructure for Smart Water Metering and Water Demand Modeling

With rapid growth of urban populations and limited water resources, achieving an appropriate balance between water supply capacity and residential water demand poses a significant challenge to water supplying agencies. With the recent emergence of smart metering technology, where water use can be monitored and recorded at high resolution (e.g., observations of water use every 5 seconds), most existing research has been aimed at providing water managers with detailed information about the water use behavior of their consumers and the performance of water using fixtures. However, replacing existing meters with smart meters is expensive, and effectively using data produced by smart meters can be a roadblock for water utilities that lack sophisticated information technology expertise. The research in this dissertation presents low cost, open source cyberinfrastructure aimed at addressing these challenges. Components developed include an open source algorithm for identifying and classifying water end use events from smart meter data, a low cost datalogging and computational device that enables existing water meters to collect high resolution data and compute end use information, and a detailed water demand model that uses end use event information to simulate residential water use at a municipality level. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah. We tested the applicability of the disaggregation algorithm in quantifying water end uses for different meter sizes and types. We tested the datalogging computational device at a residential household and demonstrated collection, disaggregation, and transfer of high resolution flow data and classified events into a secure server. Finally, we demonstrated a water demand model that simulates the detailed water end uses of Logan’s residents using a combination of a set of representative water end use events and monthly billing data. Using the data we collected and the outputs from the model, we demonstrated opportunities for conserving water through improving the efficiency of water using fixtures and promoting behavior changes

Demand Disaggregation for Non-Residential Water Users in the City of Logan, Utah, USA

Non-residential users contribute to a significant portion of the total water delivered by water supplying agencies. However, a very limited number of studies have attempted to investigate the water use behavior of non-residential users. With the emergence of newer “smart” meters, water use now can be measured and recorded at a very high temporal frequency. Smart meters can help determine total water use, timing, and component end uses to better understand water use practices by non-residential users. Water end use disaggregation is the process of separating the water used by each fixture or process within a facility. This is useful because having a breakdown of the consumption of all end uses may encourage users to consume less water and gives them indications on how to do so. This project involved collecting and working with three different datasets with three different temporal scales (monthly billing data, 5-minute water use data, and 5-second water use data). We analyzed monthly billing data to solicit potential participating facilities for the study. For each participating facility, new smart devices were installed on their existing water meters, including an advanced water meter register and a pulse counting data logger. The newer registers logged and transmitted data to a web-accessible data portal at 5-minute intervals, while the pulse counters recorded water use at 5- second intervals. These devices enabled us to measure the timing and volume of different water uses (e.g., indoor versus outdoor versus industrial processes uses). In this project, we identified different water use events, average water used by each end use (from plumbing fixtures to industrial machinery), variability in end uses (faucets/toilets versus showers), variability in use by the type of user (manufacturing facilities versus assisted living homes), and the impact of the business type on the water use

A filtering algorithm for high-resolution flow traces to improve water end-use analysis

[EN] One of the main difficulties encountered when designing automatic tools for water end use identification is the inherent noise present in recorded flow traces. Noise is mainly caused by the inability of the monitoring equipment to accurately register water consumption and data-loggers to register, without distortion, the signal received from the water meter. A universal filtering algorithm has been developed to remove noise and simplify water consumption flow traces with the aim of improving future automatic end use identification algorithms. The performance of the proposed filtering methodology is assessed through the analysis of 21,647 events. Water consumption data were sourced from two different water end use studies, having consumers and monitoring equipment with dissimilar characteristics. The results obtained show that the algorithm is capable of removing an average of 70% of the data points that constitute the flow traces of the complex events examined. The simplified flow traces allow for faster and more accurate disaggregation and classification algorithms, without losing significant information or distorting the original signal. The ability of the proposed filtering algorithm to fit the original flow traces was benchmarked using the Kling-Gupta efficiency coefficient, obtaining an average value above 0.79.This study has received funding by the IMPADAPT project/CGL2013-48424-C2-1-R from the Spanish ministry MINECO with European FEDER funds and from the European Union's Seventh Framework Programme (FP7/2007e2013) under grant agreement no. 619172 (SmartH2O: an ICT Platform to Leverage on Social Computing for the Efficient Management of Water Consumption).Pastor-Jabaloyes, L.; Arregui De La Cruz, F.; Cobacho Jordán, R. (2018). A filtering algorithm for high-resolution flow traces to improve water end-use analysis. Water Science & Technology: Water Supply. 19(2):451-462. https://doi.org/10.2166/ws.2018.090S45146219

Advancing Data Collection, Management, and Analysis for Quantifying Residential Water Use via Low Cost, Open Source, Smart Metering Infrastructure

Urbanization, climate change, aging infrastructure, and the cost of delivering water to residential customers make it vital that we achieve a higher efficiency in the management of urban water resources. Understanding how water is used at the household level is vital for this objective.Water meters measure water use for billing purposes, commonly at a monthly, or coarser temporal resolutions. This is insufficient to understand where water is used (i.e., the distribution of water use across different fixtures like toilets, showers, outdoor irrigation), when water is used (i.e., identifying peaks of consumption, instantaneous or at hourly, daily, weekly intervals), the efficiency of water using fixtures, or water use behaviors across different households. Most smart meters available today are not capable of collecting data at the temporal resolutions needed to fully characterize residential water use, and managing this data represents a challenge given the rapidly increasing volume of data generated. The research in this dissertation presents low cost, open source cyberinfrastructure (datalogging and data management systems) to collect and manage high temporal resolution, residential water use data. Performance testing of the cyberinfrastructure demonstrated the scalability of the system to multiple hundreds of simultaneous data collection devices. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah where we found significant variability in the temporal distribution, timing, and volumes of indoor water use. This variability can impact the design of water conservation programs, estimations and forecast of water demand, and sizing of future water infrastructure. Outdoor water use was the largest component of residential water use, yet homeowners were not significantly overwatering their landscapes. Opportunities to improve the efficiency of water using fixtures and to conserve water by promoting behavior changes exist among participants

Analysis and characterization of residential and non-residential water consumption at different levels of spatio-temporal detail

Incremento demografico, urbanizzazione e cambiamenti climatici stanno esercitando una pressione sempre maggiore sulle risorse idriche. In questo contesto, conoscere le distribuzioni spazio-temporali delle richieste idriche con elevato grado di dettaglio risulta essenziale per lo sviluppo di strategie finalizzate a garantire le attuali e future condizioni di domanda, sviluppare tecnologie per il risparmio idrico e promuovere iniziative di sensibilizzazione a un utilizzo consapevole. Alla luce di ciò, la recente letteratura si è arricchita di numerosi studi finalizzati a investigare i consumi idrici, fino al livello di utilizzi finali dell’acqua (end uses) e con particolare riferimento al settore residenziale, mancando tuttavia di un’analisi dettagliata degli studi esistenti, inclusiva di un confronto sistematico e di un’elaborazione omnicomprensiva dei dati riportati. Inoltre, la maggior parte dei metodi recentemente sviluppati per ottenere dati di consumo idrico a livello di end use partendo dalle informazioni raccolte a livello di intera unità abitativa risulta generalmente in grado di elaborare solo dati a risoluzioni temporali difficilmente a disposizione dei gestori idrici. Nondimeno, ad oggi, solo un numero limitato di studi ha investigato i consumi idrici in contesti non residenziali o le variazioni di consumo a fronte di circostanze straordinarie quali catastrofi o pandemie. La presente tesi si ripropone di contribuire alla caratterizzazione dei consumi idrici residenziali e non residenziali con elevato grado di dettaglio spazio-temporale e sotto diverse condizioni di domanda idrica. In primis, viene condotta un’analisi estensiva di più di cento pubblicazioni sui consumi idrici residenziali a livello end use, alla quale segue una discussione sistematica dei relativi contenuti e risultati proposti, con al fine di approfondire le caratteristiche di consumo residenziale in diversi contesti del mondo ed evidenziarne gli aspetti maggiormente investigati. Si presenta, altresì, una metodologia per la disaggregazione di dati di consumo idrico monitorato a livello di unità abitativa e la relativa classificazione nei diversi end use. La metodologia, basata su dati a una risoluzione temporale simile a quella della maggior parte degli smart meter tipicamente a disposizione dei gestori idrici, viene validata con dati raccolti in due differenti aree geografiche. La seconda parte della tesi si focalizza invece sulla caratterizzazione dei consumi idrici in contesti non residenziali non ancora investigati o sotto particolari condizioni di domanda idrica. Più nel dettaglio, viene fornita una panoramica sugli effetti del turismo balneare sui consumi idrici – analizzando l’impatto di stabilimenti balneari e case-vacanza in una zona costiera soggetta a elevate fluttuazioni turistiche – e si valuta altresì l’effetto della pandemia da COVID-19 sui consumi idrici, con riferimento a due differenti contesti urbani e fino al livello di singola utenza. I principali contributi della presente tesi risultano i seguenti: (1) viene fornito un ampio database a scala mondiale degli utilizzi finali dell’acqua a livello residenziale; (2) si presenta una metodologia robusta e versatile per la disaggregazione end use dei dati di consumo idrico, ampiamente trasferibile ad altri contesti residenziali; (3) si dimostra l’impatto del turismo balneare sui consumi idrici e sulle relative distribuzioni; (4) si quantificano gli impatti del lockdown da COVID-19 sui consumi idrici a diverse scale spazio-temporali e con riferimento a differenti tipologie di utilizzo dell’acqua. Nel complesso, i risultati della corrente tesi possono essere di interesse per meglio comprendere le principali caratteristiche del consumo idrico in diversi contesti e scenari, supportando i gestori idrici nell’efficientamento dei sistemi di distribuzione e indirizzando gli utenti a un consumo più consapevole della risorsa idrica.Population growth, urbanization, and climate changes are leading large areas under water stress. Within this framework, a detailed information on where, when, and how water is being used is an essential requirement for effective strategies aimed at meeting current and future demand. To pursue this, more attention has been recently devoted to the investigation of water consumption at fine levels of spatio-temporal detail (e.g. up to the level of individual end uses of water), the knowledge of which can aid demand modelling, technologies for water saving, or campaigns aimed at increasing people’s awareness towards consumption. As a result, the recent literature includes numerous publications exploring the residential end uses of water, but systematic comparisons and elaborations of these fragmented data are missing. Moreover, collecting and processing end-use data may be challenging, since most of the developed methods to obtain end-use information from user-level data can exploit only data collected at temporal resolutions which may be unavailable to water utilities. Also, in some cases, non-residential users may consume a more relevant quantity of water, and with profiles different from those related to the residential uses of water. However, to date, research on non-residential consumption has been mostly carried out in relation to specific users, and typically at very coarse temporal resolutions. Nevertheless, it has scarcely focused on water consumption in the event of non-ordinary situations affecting people’s habits or the operational conditions of water distribution systems, such as disasters or pandemics. The aim of this thesis is to take a step forward in the characterization of residential and non-residential water consumption at different levels of spatio-temporal detail, and by considering different demand conditions. First, with reference to the residential sector, a comprehensive analysis of more than one hundred end-use studies conducted worldwide is carried out – along with an in-depth discussion of their scope, features, and results – to investigate the main perspectives from around the world and highlighting which aspects have been mostly explored. In addition, an automated method for residential end-use disaggregation and classification – exploiting user-level data collected at a sampling resolution which is close to that of the smart meters available to water utilities – is developed and validated with data from two different geographical areas. Second, the water consumption of some still unexplored non-residential contexts, or under non-ordinary demand conditions, is investigated: in greater detail, insight into the effects of seaside tourism on water consumption is provided by exploring the impacts of bathing facilities and holiday homes in coastal area subjected to high tourist fluctuations, whereas an overview of the effects of COVID-19 pandemic on water consumption is provided in relation to different urban contexts for which analyses are conducted up to the level of individual users. Overall, the current thesis: (1) provides an extensive, worldwide database of residential end uses of water from which many future studies can be developed; (2) presents a generalized and robust method for end-use disaggregation and classification, widely applicable to several residential water consumption contexts; (3) demonstrates the effects of seaside tourism on water consumption and its profiles; and (4) quantifies the impacts of the lockdown imposed to limit the spread of COVID-19 on water consumption based on multiple temporal scales and in relation to different types of consumption. This thesis's findings can aid water utilities and their users in better understanding the major characteristics of water consumption in different contexts and scenarios, supporting the formers in efficiently managing water systems, and encouraging the latter to a more conscious use of water resources

Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems

[EN] This document is intended to be a presentation of the Special Issue "Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems". The final aim of this Special Issue is to propose a suitable framework supporting insightful hydraulic mechanisms to aid the decision-making processes of water utility managers and practitioners. Its 18 peer-reviewed articles present as varied topics as: water distribution system design, optimization of network performance assessment, monitoring and diagnosis of pressure pipe systems, optimal water quality management, and modelling and forecasting water demand. Overall, these articles explore new research avenues on urban hydraulics and hydroinformatics, showing to be of great value for both Academia and those water utility stakeholders.Herrera Fernández, AM.; Meniconi, S.; Alvisi, S.; Izquierdo Sebastián, J. (2018). Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems. Water. 10(4):1-7. https://doi.org/10.3390/w10040440S1710

Household flow detection using FEAT (flow estimating accelerometer-thermometer) device

This is the final version. Available from Elsevier via the DOI in this record. Data availability: The authors do not have permission to share data.The use of IoT devices in water end use disaggregation verification is an emerging field which offers benefits over conventional approaches, in terms of cost, accuracy and scalability. Having reliably disaggregated water appliance consumption data will enable smart water meter data to be used in household water conservation approaches and for understanding water consumption behaviours. The FEAT device provides a low cost, easily applied and scalable solution that is demonstrated to work even for very low flow conditions of 0.03 l/s. The FEAT device is a combination of a battery, Wi-fi board and MPU6050 sensors providing multi-modal accelerometer and thermometer data. The study places 7 of these FEAT devices onto hot and cold water pipes leading to a shower, which is operated 4 times in a high flow situation, 0.13 l/s, and 4 times in a low flow situation, 0.03 l/s. The data is then analysed and compared with a flow logger to determine if the FEAT device can detect when a domestic appliance is using water. There are limiting cases where the level of noise or external interference limits distorts the data, obscuring the distinguishable peaks in the data due to the similarity of the values. By using high and low pass filtering methods it was possible to enhance the peaks but there are still situations where peaks cannot be detected: for example, if a rigid pipe is not able to vibrate easily or if a hot water boiler is not triggered due to the low flow rate. However, the results show it should be possible to overcome these limiting cases, as it is much less likely for both the vibration and temperature data to be adversely affected by noise or external influences simultaneously, therefore decreasing the effect of noise and external influences. In conclusion, this research paper demonstrates that FEAT devices are a low cost, easily applied and scalable solution for detecting flow. By using high and low pass filtering, placing sensors on freely moving pipes and through the use of multi-modal verification, the FEAT device is shown to work on both metal and plastic pipes even in the lowest flow situations of 0.03 l/s. Therefore the FEAT device is a suitable solution for appliance identification in disaggregation verification datasets.Engineering and Physical Sciences Research Counci

Simplifying water consumption flow traces for improving end use recognition: a case study

[EN] The success of automatic water end use disaggregation and classification strategies greatly depends on the filtering and signal conditioning of the flow traces recorded. The work presented proposes a new filtering algorithm of water consumption flow traces. To improve the performance of the filter, the parameters driving the process are found per event by an automatically calibration procedure. These parameters are selected to ensure the maximum adaptability and simplification of the filtered flow traces. The methodology has been tested with 5210 consumption events obtained from a measurement campaign conducted in a Spanish city. The results obtained show that the filtering algorithm is capable of significantly simplifying the original flow traces while maintaining their main characteristics. On average, it has been found that the most complex events can be described using only 10% of the input data. This analysis can be used to make more efficient the filtering procedure proposed.[ES] El éxito de estrategias para la desagregación y clasificación automática de los consumos de agua en usos finales depende de un adecuado filtrado previo de las trazas de caudal registradas. Se propone un nuevo algoritmo de filtrado, cuyos parámetros de entrada se ajustan mediante un proceso de calibración automático por evento de consumo, asegurando la adaptabilidad y simplificación de la traza filtrada a la original. Esta herramienta se aplica a un caso de estudio mediante el análisis de 5210 eventos de consumo, procedentes de una campaña de monitorización en una ciudad española. Los resultados muestran que el filtro es capaz de simplificar sustancialmente las trazas de caudal manteniendo la información esencial. En media, las trazas de caudal de eventos más complejos pueden definirse con menos del 10% de los puntos de las trazas originales. Además, el análisis realizado permite identificar diversas estrategias para mejorar y optimizar el proceso de filtrado.El trabajo presentado en este artículo ha sido posible gracias al Proyecto IMPADAPT/CGL2013-48424-C2-1-R del Ministerio de Economía y Competitividad de España con fondos FEDER y al VII Programa Marco de la Unión Europea, bajo el acuerdo de financiación no. 619172(SmartH2O: an ICT Platform to leverage on Social Computing for the efficient management of Water Consumption).Pastor Jabaloyes, L.; Arregui De La Cruz, F.; Cobacho Jordán, R. (2018). Mejora del reconocimiento de usos finales del agua mediante la simplificación de la traza de caudal: un caso de estudio. Ingeniería del Agua. 22(4):195-208. https://doi.org/10.4995/ia.2018.9476SWORD195208224Arregui, F. (2015). New software tool for water End-Uses studies. Presentation of 8th IWA International Conference on Water Efficiency and Performance Assessment of Water Services, Cincinnati, USA.Cominola, A., Giuliani, M., Piga, D., Castelletti, A., Rizzoli, A.E. (2015). Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review. Environmental Modelling & Software, 72, 198-214, https://doi.org/10.1016/j.envsoft.2015.07.012DeOreo,W.B., Heaney, J.P., Mayer, P.W. (1996). Flow trace analysis to assess water use. American Water Works Association, 88, 79-90. https://doi.org/10.1002/j.1551-8833.1996.tb06487.xFielding, K.S., Spinks, A., Russell, S., McCrea, R., Stewart, R.A., Gardner, J. (2013). An experimental test of voluntary strategies to promote urban water demand management. Journal of Environmental Management, 114, 343-351. https://doi.org/10.1016/j.jenvman.2012.10.027Gupta, H.V., Kling, H., Yilmaz, K.K., Martinez, G.F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377, 80-91,https://doi.org/10.1016/j.jhydrol.2009.08.003Kowalski, M., Marshallsay, D. (2003). A System for Improved Assessment of Domestic Water Use Components. II International Conference Efficient Use and Management of Urban Water Supply, International Water Association, Tenerife, Spain.Larson, E., Froehlich, J., Campbell, T., Haggerty, C., Atlas, L., Fogarty, J., Patel, S.N. (2012). Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments. Pervasive and Mobile Computing, 8, 82-102. https://doi.org/10.1016/j.pmcj.2010.08.008Nguyen, K.A., Zhang, H., Stewart, R.A. (2013a). Development of an intelligent model to categorise residential water end use events. Journal of Hydro-environment Research, 7, 182-201. https://doi.org/10.1016/j.jher.2013.02.004Nguyen, K.A., Stewart, R.A., Zhang, H. (2013b). An intelligent pattern recognition model to automate the categorisation of residential water end-use events. Environmental Modelling & Software, 47, 108-127. https://doi.org/10.1016/j.envsoft.2013.05.002Pastor-Jabaloyes, L., Arregui, F.J., Cobacho, R. (2018). Water End Use Disaggregation Based on Soft Computing Techniques. Water, 10(1), 46. https://doi.org/10.3390/w10010046R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Disponible en: http://www.R-project.org/.UNEP (United Nations Environment Programme). (2011). Water: Investing in Natural Capital. UNEP, Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication, Nairobi