An enhanced method for automated end-use classification of household water data

An accurate estimation of residential end uses of water is helpful in developing efficient water systems. If not obtainable through direct metering, this information can be gathered by disaggregating and classifying household-level water-use data. However, most automated techniques require fine-resolution data (e.g., 1 s) and end-use parameters which may be unavailable to water utilities. To fill the above gap, this study presents a method for the automated disaggregation and classification of indoor water-use data collected at the 1-min temporal resolution, and by exclusively relying on the end-use parameter values available in the literature. Specifically, the features of each water-use event detected at the household level are compared against the most common event features for the selected end-use category. The results obtained by testing the method with real data collected at 14 households in two different countries (Italy and the Netherlands) confirm its potential in disaggregating and classifying water end-use events with an average accuracy higher than 90% and an average (normalized) root-mean-square lower than 0.06 despite the lack of information about end uses in individual households. This demonstrates that end-use detection is possible even with data whose resolution is closer to that of most commercial water meters. HIGHLIGHTS An enhanced method for water end-use classification is presented. The method processes household water-use data collected at the 1-min resolution. Method performance is evaluated with data collected in different spatial contexts. General, literature-based, end-use parameter values are applied to test the method. Method average accuracy is demonstrated to be higher than 90%.