Poster Details

Abstract

To date, the terminology of "smart water meter" has been used liberally to describe devices that are capable of measuring and recording water use data at high spatial and temporal frequencies. However, the use of such meters has mostly been limited to data collection and leak detection with much of the "intelligence" of smart meters (e.g., the promise of automated procedures for extracting actionable information from high resolution data) going unrealized. Additionally, most residential meters in use today are analog, without high resolution data collection capabilities or additional data processing capabilities required for a meter to be "smart". With the rapid development of technology for collecting and recording data, existing analog meters can be transformed into intelligent, computational nodes capable of "shrinking" the big data sets they produce into decision-relevant information that can be more easily transmitted - i.e., much smaller data volume requiring no post processing that can be immediately acted upon by both water managers and consumers. In an effort to advance the current smart metering applications, we developed a 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. 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.