<p>Water end use disaggregation aims to separate household water consumption data collected from a single residential water meter into appliance/fixture-level consumption data. In recent years, the field has rapidly expanded as the value of disaggregated data has been shown for understanding water use behavior, identifying anomalies, and identifying opportunities for conserving water. Several methods have been developed for disaggregating water end uses from high temporal resolution water use data collected using residential smart water meters. However, most existing methods have been incorporated into proprietary software tools and have been tested using datasets that are inaccessible due to privacy issues, with the result being that neither the code nor the data from these studies are available for verification or potential reuse. We describe and demonstrate a new, open source, and reproducible water end use disaggregation and classification tool that builds upon the results of existing smart water metering and end use disaggregation studies. The tool was designed and developed in Python and can be accessed via any current Python programming environment. It was tested on anonymized, high temporal resolution datasets collected from 31 residential dwellings located in the Cities of Logan and Providence, Utah, USA for a period of one month. Results from different meter types and sizes were tested to demonstrate the accuracy and reproducibility of the tool in disaggregating and classifying high temporal resolution data into individual water end use events. Execution of the tool requires approximately one minute for processing one-day of data collected at a four second time interval for one dwelling. The disaggregation tool is open source and can be adapted to specific research needs. The anonymized dataset we used to develop and test the tool is openly available in the HydroShare data repository.</p>
PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys
