We present a new, open source, computationally capable datalogger for collecting and analyzing high temporal resolution residential water use data. Using this device, execution of water end use disaggregation algorithms or other data analytics can be performed directly on existing, analog residential water meters without disrupting their operation, effectively transforming existing water meters into smart, edge computing devices. Computation of water use summaries and classified water end use events directly on the meter minimizes data transmission requirements, reduces requirements for centralized data storage and processing, and reduces latency between data collection and generation of decision-relevant information. The datalogger couples an Arduino microcontroller board for data acquisition with a Raspberry Pi computer that serves as a computational resource. The computational node was developed and calibrated at the Utah Water Research Laboratory (UWRL) and was deployed for testing on the water meter for a single-family residential home in Providence City, UT, USA. Results from field deployments are presented to demonstrate the data collection accuracy, computational functionality, power requirements, communication capabilities, and applicability of the system. The computational node’s hardware design and software are open source, available for potential reuse, and can be adapted to specific research needs.

Residential water end-use events (e.g., showers, toilets, faucets, etc.) can be derived from high temporal resolution (<1 min) water metering data. Past studies have collected data at different temporal resolutions (e.g., 4 s, 5 s, or 10 s) without assessing the impact of the temporal aggregation interval on end-use event features (e.g., volume, flowrate, duration) due to the unavailability of data at a sufficient temporal resolution to enable such analyses. We recorded the time between every magnetic pulse generated by a magnetically driven residential water meter’s measurement element (full pulse resolution) using a new, open-source datalogging device and collected data for two residential homes in Utah, USA. We then examined water use events without temporally aggregating data and compared to the same data aggregated at different time intervals to evaluate how temporal resolution of the data affects our ability to identify end-use events, calculate features of individual events, and classify events by end use. Our results show how collecting full pulse resolution data can provide more accurate estimates of event occurrence, timing, and features along with producing more discriminative event features that can only be estimated from full pulse resolution data to make event classification easier and more accurate.