Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review
Introduction
World's urban population is expected to raise from current 54%–66% in 2050 and to further increase as a consequence of the unlikely stabilization of human population by the end of the century (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., 60–80% in Europe (Collins et al., 2009), 58% in the United States (Kenny et al., 2009)).
The concentration of the water demands of thousands or millions 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 strategies (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 conditions) and demand management actions (e.g., water restrictions, pricing schemes, education campaigns) are essential to explore water users' response to alternative WDMS, ultimately supporting strategic planning and policy design.
Traditionally, water demand models focus on different temporal and spatial scales. At the lowest resolution, studies have been carried out, mostly in the 1990s, to model water demand at the urban or block group scale, using low time resolution (i.e., above daily) consumption data retrieved through billing databases or experimental measurement campaigns on a quarterly or monthly basis. The main goal of these works is to inform regional water systems planning and management on the basis of estimated relationships between water consumption patterns and socio-economic or climatic drivers (e.g., House-Peters and Chang, 2011).
The advent of smart meters (Mayer and DeOreo, 1999) in the late 1990s made available new water consumption data at very high spatial (household) and temporal (from several minutes up to few seconds) resolution, enabling the application of data analytics tools to develop accurate characterizations of end-use water consumption profiles. Similarly to the recent developments in integrated smart solutions (Hilty et al., 2014, Laniak et al., 2013), the use of smart meters provides essential information to construct models of the individual consumers behaviors, which can be employed for designing and evaluating consumer-tailored WDMS that can more effectively modify the users' attitude favoring water saving behaviors. In particular, smart meters themselves constitute technologies that promote behavioral changes and water saving attitudes via tailored feedbacks (Fielding et al., 2013).
A general procedure to study residential water demand management relying on the high-resolution data nowadays available can be structured in the following four phases (see Fig. 1): (i) data gathering, (ii) water end-uses characterization, (iii) user modeling, (iv) design and implementation of personalized WDMS. In the literature, a number of tools and techniques have been proposed for each of these steps, with many works focused either on the data gathering process (e.g., Cordell et al., 2003, Boyle et al., 2013) or on the analysis of WDMS (e.g., Inman and Jeffrey, 2006). Yet, to the authors' knowledge, a systematic and comprehensive review of residential water demand modeling and management is still missing. This review contributes the first effort of classification and critical analysis of 134 studies that in the last 25 years (Fig. 2) contributed new methodologies and tools in one or more of the steps of the above procedure (see Table 1).
The review is structured according to the procedure shown in Fig. 1: the current status, research challenges, and future directions associated to each phase are discussed in Sections 2 Data gathering, 3 Water end-uses characterization, 4 User modeling, 5 Personalized water demand management strategies, while the last section reports final remarks and directions for follow up research.
Section snippets
Data gathering
Residential water consumption data gathering (box 1 in Fig. 1) represents the first step needed to built the baseline upon which the water demand is estimated and management strategies are designed. Depending on the sampling frequency, we distinguish two main classes, namely low-resolution and high-resolution data, which delimit the type of the analysis that can be performed.
Water end-uses characterization
Non-intrusive metering requires disaggregation algorithms to breakdown the total consumption data registered at the household level into the different end-use categories (second block of Fig. 1). In the water research literature, several studies have been conducted in the last two decades using a variety of single or mixed disaggregation methods, such as household auditing, diaries, high resolution flow meters and pressure sensors (see Table 3). According to the methodology adopted, we can
User modeling
The user modeling phase (third block in Fig. 1) aims at representing the water demand at the household level, thus preserving the heterogeneity of the individual users in the modeled community, possibly as determined by natural and socio-psychographic factors as well as by the users' response to different WDMS. In the literature, two distinctive approaches exist (see Table 4): descriptive models, which limit their extent to the analysis of water consumption patterns, and predictive models,
Personalized water demand management strategies
Literature reports of a variety of management policies acting on the demand side of residential water consumption, designed with the purpose of improving water conservation and safeguarding water security in urban contexts. According to Inman and Jeffrey (2006), they can be classified in the following five categories (Table 5): technological, financial, legislative, maintenance, and educational. These strategies differ in the time scales they act on: price and prescriptive (i.e.,
Discussion and conclusions
Designing and implementing effective water demand management strategies is becoming more and more important to secure reliable water supply and reduce water utilities' costs over the next years. The advent of smart meters made available new water consumption data at very high spatial and temporal resolution, enabling a more detailed description of the drivers inducing people to consume or save water. A better understanding of water users' behaviors is indeed fundamental to promote water savings
Acknowledgments
The work was supported by the SmartH2O: an ICT Platform to leverage on Social Computing for the efficient management of Water Consumption research project funded by the EU Seventh Framework Programme under grant agreement no. 619172.
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