Household water end-use identification in the presence of rudimentary data
Thesis (PhD)--Stellenbosch University, 2021.
ENGLISH ABSTRACT: Detailed and accurate information regarding residential water use is essential for targeted water demand management (WDM) strategies and water security, and yet most utilities have limited information regarding household water demand at end-use level. Flow trace analysis software has been successfully deployed to disaggregate household water end-uses from high resolution smart meter data in various earlier studies, however, water utilities from a range of socio-economic settings, especially in developing countries, typically measure household water consumption data at resolutions too low for commercially available disaggregation software. The aim of this research was to identify and develop methods to evaluate and quantify household water demand at an end-use level, in the absence of high resolution data. Numerous end-use studies were conducted using direct methods (i.e. water meters) and indirect methods (e.g. temperature loggers) to record residential water demand at the point of entry and at the point of use. Valuable information was extracted from the recorded time series data by applying the automated temperature analysis algorithm, with end-use event durations and event frequencies being derived from the results. Numerous benefits and limitations regarding temperature loggers as indirect method were addressed as part of this research. Additionally, measurements were taken at a single entry point on a residential property. An automated end-use extraction tool (PEET) and classification model (WEAM) were developed to identify and categorise residential end-use events from a rudimentary data set. Despite the coarse resolution of the measured data making it impossible to separately classify background leakage and relatively low flow water use events (consequently categorising both instances as minor events), PEET was able to extract notable end-use events from the study site. The WEAM model was able to correctly classify the notable end-use events into indoor use and outdoor use categories. The methods and models proposed as part of this research could enable utilities to broadly classify household end-use events as being indoor or outdoor, without relying on pre-trained models. By applying the developed models on rudimentary data sets, water managers could improve water security through better informed demand management programmes.
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