Olea Edge Analytics, a provider of intelligent solutions and services for the water utility industry, has announced a pilot programme with Sugar Land, Texas, to diagnose the health of large commercial and industrial water metres.
The City of Sugar Land will place Olea’s Meter Health Analytics (MHA) solution on 25 large commercial metres in its systems, looking for signs of water loss, inaccurate readings and malfunctioning components. These large meters typically make up only 10% of a utility’s fleet, but a single commercial metre failure is equivalent to thousands or even tens of thousands of residential meter failures. Still, most utilities are only able to test these meters through flow testing once every 1-3 years, meaning many performance deficits may linger for years before being identified. This can lead to challenges regarding water loss and accurate metering and billing.
“Flow testing is useful, but it is typically done infrequently, and while it can tell whether or not a meter is accurate, it does not provide a diagnosis for why,” Olea Edge Analytics CFO Jennifer Crow says. “As a fast-growing city, Sugar Land wanted a more dynamic and comprehensive view of their largest meters, to make data-driven decisions about asset management and to better serve their customers.”
MHA collects data from sensors mounted directly on a metre. That data is then analysed to identify and diagnose performance issues with a new level of precision, giving utilities never-before-seen insights to optimise their largest meters and enabling field teams to know exactly what’s wrong so they can quickly address it. MHA also enables utilities to ensure accurate meter performance in between flow tests. This further supports accurate billing and reduces water loss, which remains a big concern for Texas utilities as the state remains mired in a sustained drought. According to recent data from the U.S. Drought Monitor, nearly three-quarters of the state (73.2%) is experiencing drought.
To learn more about how Olea Edge Analytics helps utilities control water loss through sensors that combine AI, machine learning, and edge computing technology, visit here.
Comment on this article below or via Twitter: @IoTNow_OR @jcIoTnow
