Water Event Categorization Using Sub-Metered Water and Coincident Electricity Data

Abstract

This study evaluated the potential for data from dedicated water sub-meters and circuit-level electricity gauges to support accurate water end-use disaggregation tools. A supervised learning algorithm was trained to categorize end-use events from an existing database consisting of features related to whole-home and hot water use. Additional features were defined based on dedicated irrigation metering and circuit-level electricity gauges on major water appliances. Support vector machine classifiers were trained and tested on portions of the database using multiple feature combinations, and then externally validated on water event data collected under dissimilar conditions from a demonstration house in Austin, Texas, USA. On the testing data, a trained classifier achieved true positive rates for occurrences and volume exceeding 95% for most categories and 93% for toilet events. Performance for faucet events was less than 90%. Initial results suggest that dedicated sub-meters and circuit-level electricity gauges can facilitate highly accurate categorization with simple features that do not rely on flow rate gradients.

Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets

“…First, there is an inverse correlation between the dataset size (or the time series length) and the time sampling resolution. Datasets comprising hundreds or thousands of homes (e.g., [48,49,[64][65][66]) generally include data collected with a monthly or daily time sampling resolution, while datasets with a sub-daily time sampling resolution only include a few units or tens of homes (e.g., [67][68][69]). This may be attributed to the experimental extent of most high-resolution studies, their usually short-term duration, and the costs of deploying large-scale smart metering systems.…”

“…The distribution of the end use datasets in Figure 5 is an empirical validation of the findings of a previous study by Cominola et al [28], which demonstrated that only data gathered with time sampling resolutions of a few seconds or, at most, 1 min, can be used to accurately estimate the contribution, peak, and time of use of individual water fixtures, especially when multiple end uses are active. Besides facilitating accurate end use disaggregation [67][68][69][156][157][158], such high resolution data also allow a detailed characterization of consumer behaviors [77,155,159,160], and the design of customized water demand strategies [88,123,142,161,162].…”

Automated Household Water End-Use Disaggregation through Rule-Based Methodology

“…pressure data) have been proposed as well (e.g. Kim et al 2008;Froehlich et al 2009Froehlich et al , 2011Srinivasan et al 2011;Ellert et al 2015;Vitter and Webber 2018).…”

Variability in Consumption and End Uses of Water for Residential Users in Logan and Providence, Utah, US

“…Despite the number of algorithms described in the literature, opportunities to replicate or build from these tools are limited due to the unavailability of code and/or data. Di Mauro et al (2020) found that, of 41 data sets collected for assessing end uses of water at residential properties, only four (Beal and Stewart 2011;Makonin 2016;Vitter and Webber 2018;Kofinas et al 2018) had an open access policy. In limited instances, flow trace data (i.e., the raw, high resolution data collected) and event files (i.e., end use events and their attributes extracted from raw data) from past studies were available for purchase (Aquacraft 2016), including the events table resulting from the one of the largest studies of water end uses conducted to date (DeOreo et al 2016).…”