By Brian Fiut
As utilities move from monthly - or in some cases quarterly or annual - meter reads to Advanced Metering Infrastructure (AMI) systems, which offer more granular reads (such as daily or hourly), the collected information becomes much more than simply billing data; it becomes actionable intelligence which can be utilized by departments across the utility, and it gives utilities insight into buying behavior and system response.
Buying Behavior - Based on this data, the utility now has much greater awareness of when the end user buys water, how much they are willing to pay, how much they use at any given time, and whether they have responded to any specific incentive, conservation or other marketing programs.
System Response - Additionally, based on this data, the utility now has much greater insight into how their distribution system is responding to demand. This gives utilities the opportunity to spot bottlenecks, detect patterns representative of theft or non-revenue water (NRW), manage overall system integrity, and help customers understand their own consumption patterns.
When earlier Automated Meter Reading (AMR) technology was first introduced, system benefits were primarily focused on automation of the collection of billing data. Indeed, automated Meter Data Collection (MDC) was the "killer app" of its day and has largely demonstrated the value of the automating the meter reading process.
Next in the evolution of meter data use was the ability to manage not only meter reads, but all of the data associated with the meter. The concept of Meter Data Management (MDM) evolved primarily in an effort to offer a more precise way to create billing determinants based on actual Time of Use (TOU) data rather than across a broad monthly or quarterly time span. Behind this technology was a whole raft of innovations including the ability to Validate, Estimate and Edit (VEE) the incoming data stream.
As utilities began storing large amounts of granular data using their MDM systems, they began to notice that this data had greater potential beyond merely enhancing billing options for the utility. They began to notice that this data was a window into how the utility's customers were consuming their commodity, as well as how their distribution network was performing in the delivery of this commodity.
The key value propositions, which have evolved from this trend, include enhancing the customer service experience, improving protection and vigilance of the revenue stream, conducting a variety of technical distribution system analyses, actively managing a conservation program or campaign, mining the collected data for a variety of advanced analyses, and finally, giving the consumer insight into their own consumption patterns and behaviors.
