Extracting household water use event characteristics from rudimentary data

Abstract

Abstract Household water end-uses have been extracted from high-resolution smart water meter data in various earlier studies. However, research on end-use disaggregation from rudimentary data is limited. Rudimentary data is defined as data recorded in intervals longer than 1 min, or data recorded with resolutions larger than 0.1 L/pulse. Developing countries typically deal with rudimentary data, due to the high cost and high resource investment associated with high-resolution data. The aim of this study was to extract useful event characteristics from rudimentary data, without identifying the actual end-uses per se. A case study was conducted in the City of Johannesburg, South Africa, where 63 homes were equipped with iPERL smart water meters. The meters recorded flow measurements every 15 s at a 1 L/pulse resolution, rendering the recorded data rudimentary. A total of 1,107,547 event pulses were extracted over the 217-day study period. Although the method presented is limited in the sense that water use events cannot be identified, the method allows for disaggregation of event pulses in the presence of rudimentary data. Using this tool, it is possible to lift valuable information from rudimentary data that would subsequently benefit service providers in setting water demand strategies.