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

Resumen

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.