Implications of data sampling resolution on water use simulation, end-use disaggregation, and demand management. (April 2018)

• Record Type: Journal Article
• Authors: Cominola, A.; Giuliani, M.; Castelletti, A.; Rosenberg, D.E.; Abdallah, A.M.
• Is Part Of: Environmental modelling & software. Volume 102(2018)
• Page Start: 199
• Page End: 212
• Publication Date: 2018-04
• Subjects: Smart meter -- Sampling resolution -- Water demand management -- STREaM -- Synthetic end-use model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.11.022
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: We assess information from water use data at different time sampling resolutions. STREaM open source model generates residential water end uses with maximum sampling frequency of 10 s. We trade off benefits, costs, and feasibility of high-resolution water meters. Our multi-resolution assessment supports research, utilities, and meter deployments.