Development of an intelligent model to categorise residential water end use events

Abstract

The aim of this study was to disaggregate water flow data collected from high resolution smart water meters into different water end use categories. The data was obtained from a sample of 252 residential dwellings located within South East Queensland (SEQ), Australia. An integrated approach was used, combining high resolution water meters, remote data transfer loggers, household water appliance audits and a selfreported household water use diary. Disaggregating water flow traces into a registry of end use events (e.g. shower, clothes washer, etc.) is predominately a complex pattern matching problem, which requires a comparison between presented patterns and those contained with a large registry of categorised end use events. Water flow data collected directly from water meters includes both single (e.g. shower event occurring alone) and combined events (i.e. an event which comprises of several overlapped single events). To identify these former mentioned single events, a hybrid combination of the Hidden Markov Model (HMM) and the Dynamic Time Warping algorithm (DTW) provided the most feasible and accurate approach available. Additional end use event physical context algorithms have been developed to aid accurate end use event categorisation. This paper firstly presents a thorough discussion on the single water end use event analysis process developed and its internal validation with a testing set. This is followed by the application of the developed approach on three independent households to examine its degree of accuracy in disaggregating two weeks of residential flow data into a repository of residential water end use events. Future stages of algorithm development and testing is discussed in the final section.