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

J Vitter,Michael Webber

doi:10.3390/w10060714

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.

Highlights

Water end-use disaggregation has emerged as a promising tool for urban water demand management, accompanied by increasing adoption of smart water meters among water utilities [1].The goal of water end-use disaggregation is to contextualize water use data by providing information about activities and fixture types related to water use, primarily in the residential sector [2].Contextualized information about residential water end uses has a variety of applications for consumers, water utilities, and policy makers [2]
A subset of 94 homes were selected for hot water use monitoring [30]
This study provided an initial test of general model validity by applying Support vector machine (SVM) classifiers to data gathered from a demonstration home that were collected in dissimilar conditions relative to the REU2016 study

Summary

Introduction

Contextualized information about residential water end uses has a variety of applications for consumers, water utilities, and policy makers [2]. Appliance-specific information can help end users by improving perception accuracy of water use [3] while identifying pathways to meet efficiency goals or reduce water bills [4]. End-use disaggregation has potential to improve day-to-day operations [5], for example by assisting the resolution of billing disputes, facilitating improved pump scheduling to achieve greater system efficiency [6], or reducing uncertainty associated with making long-term planning decisions [2]. For policy-makers, disaggregated water use information can enable efficiency programs for both energy and water by targeting specific appliances within high-usage homes [7,8], or by providing a tool for digital multi-service utility providers to better understand their networks [9]

Methods

Results

Conclusion