Document Type
Article
Journal/Book Title/Conference
Environmental Modelling and Software
Volume
102
Publisher
Elsevier
Publication Date
4-2018
First Page
199
Last Page
212
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.
Recommended Citation
Cominola, A.; Giuliani, M.; Castelletti, A.; Rosenberg, David E.; and Abdallah, Adel M., "Implications of Data Sampling Resolution on Water Use Simulation, End-Use Disaggregation, And Demand Management" (2018). Civil and Environmental Engineering Faculty Publications. Paper 3587.
https://digitalcommons.usu.edu/cee_facpub/3587
