Implications of Data Sampling Resolution on Water Use Simulation, End-Use Disaggregation, And Demand Management

Understanding the tradeoff between the information of high-resolution water use data and the costs of smart meters to collect data with sub-minute resolution is crucial to inform smart meter networks. To explore this tradeoff, we first present STREaM, a STochastic Residential water End-use Model that generates synthetic water end-use time series with 10-s and progressively coarser sampling resolutions. Second, we apply a comparative framework to STREaM output and assess the impact of data sampling resolution on end-use disaggregation, post meter leak detection, peak demand estimation, data storage, and meter availability. Our findings show that increased sampling resolution allows more accurate end-use disaggregation, prompt water leakage detection, and accurate and timely estimates of peak demand. Simultaneously, data storage requirements and limited product availability mean most large-scale, commercial smart metering deployments sense data with hourly, daily, or coarser sampling frequencies. Overall, this work provides insights for further research and commercial deployment of smart water meters

The determinants of household water consumption: A review and assessment framework for research and practice

Achieving a thorough understanding of the determinants of household water consumption is crucial to support demand management strategies. Yet, existing research on household water consumption determinants is often limited to specific case studies, with findings that are difficult to generalize and not conclusive. Here, we first contribute an updated framework for review, classification, and analysis of the literature on the determinants of household water consumption. Our framework allows trade-off analysis of different criteria that account for the representation of a potential water consumption determinant in the literature, its impact across heterogeneous case studies, and the effort required to collect information on it. We then review a comprehensive set of 48 publications with our proposed framework. The results of our trade-off analysis show that distinct groups of determinants exist, allowing for the formulation of recommendations for practitioners and researchers on which determinants to consider in practice and prioritize in future research

Urban water consumption at multiple spatial and temporal scales. A review of existing datasets

Over the last three decades, the increasing development of smart water meter trials and the rise of demand management has fostered the collection of water demand data at increasingly higher spatial and temporal resolutions. Counting these new datasets and more traditional aggregate water demand data, the literature is rich with heterogeneous urban water demand datasets. They are characterized by heterogeneous spatial scales—from urban districts, to households or individual water fixtures—and temporal sampling frequencies—from seasonal/monthly up to sub-daily (minutes or seconds). Motivated by the need of tracking the existing datasets in this rapidly evolving field of investigation, this manuscript is the first comprehensive review effort of the state-of-the-art urban water demand datasets. This paper contributes a review of 92 water demand datasets and 120 related peer-review publications compiled in the last 45 years. The reviewed datasets are classified and analyzed according to the following criteria: spatial scale, temporal scale, and dataset accessibility. This research effort builds an updated catalog of the existing water demand datasets to facilitate future research efforts end encourage the publication of open-access datasets in water demand modelling and management research

Long-term water conservation is fostered by smart meter-based feedback and digital user engagement

Consumption-based feedback has been demonstrated to encourage water conservation behaviors. Smart meters and digital solutions can support customized feedback and reinforce behavioral change. Yet, most of the studies documenting water conservation effects induced by feedback and smart meter data visualization evaluate them in short-term experimental trials only. Here we show that water conservation behaviors promoted by smart meter-based consumption feedback and digital user engagement interventions might persist in the long term. We developed an analysis of 334 households in Valencia, Spain. We find that approximately 47% of the households engaged in our water conservation program achieved a long-term 8% reduction of volumetric water consumption, compared with pre-treatment observations. Water conservation behaviors persisted more than two years after the beginning of the program, especially for the households receiving sub-daily smart meter information. Our results provide empirical evidence that smart meter-based water consumption feedback and digital user engagement can effectively promote durable conservation behaviors