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

An assessment framework for classifying determinants of household water consumption and their priorities 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 contribute a framework for review, classification, and analysis of the literature on the determinants of household water consumption. Firstly, we identify a comprehensive set of 48 relevant publications, based on a systematic paper search. The framework firstly classifies household determinants into observable (physically seen/measured aspects of the house), latent (relates to the way occupants think/act/feel) and external (external to house and influence at regional level). Secondly, we undertake a 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. The results of our trade-off analysis show that distinct groups of determinants exist, allowing for the formulation of four recommendation categories. These provide guidance for practitioners on which determinants to consider in practice and for researchers to prioritize in future research (Figure 1).A. Cominola, L. Preiss, M. Thyer, H. R. Maier, P. Prevos, R. Stewart, and A. Castellett

Smart urban water networks: Solutions, trends and challenges

This Editorial presents the paper collection of the Special Issue (SI) on Smart Urban Water Networks [...]</jats:p

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

Data-driven Modelling of Urban Water Demand in Major European Cities: the Case Study of Milan, Italy.

The combined effect of population growth, urbanization, economic development, and climate change is changing the spatial and temporal patterns of water demands and exacerbating the stress on water resources. The global gap between water supply and demand is projected to reach 40% by 2030. Concerns on water security are dominant in cities with dense populations and more socio-economic activities. In this context, urban water demand management (UWDM) has emerged as a key measure to complement supply-side interventions to address water scarcity and governance issues in big European cities. Yet, there is still a lack of research fusing and comparing water demand characteristics and UWDM strategies across heterogeneous European contexts. A comparative and comprehensive urban water demand analysis at high spatial and temporal resolutions in major European urban centers could contribute to a better understanding of how different environmental, socio-economic, and political factors influence water use patterns, facilitating the scaling up of fine-scale UWDM practices into integrated regional, national, and European models. Here, we develop a holistic modeling approach incorporating trend detection, input identification, pattern characterization, and demand forecasting to understand the long- and short-term water demand dynamics. The first city we investigate is Milan, Italy. We explore the historical monthly water demands at the district level in 2017-2020 and daily data from individual meters in 2019-2021. Two main water use sectors can be distinguished: multiple households residential and commercial, industrial, and institutional buildings. Our preliminary results show a declining trend of total water use in 2017-2020, with a breakpoint identified at the end of 2019. Our comparative modeling study also shows that a hybrid model combining wavelet transform technique and artificial neural networks can achieve the best performance on 1-day short-term water forecast based on historical water demand only. Adding either temperature or precipitation variable does not improve the forecast accuracy. In the next steps, we will cross-correlate socio-demographic variables with water demands and apply our method to evaluate the urban development impact on water use patterns and inform efficient UWDM strategies