Deputizing Data: Using AMI for Revenue Protection

By Betsy Loeff, contributing writer

For almost as long as utilities have been installing automated meter reading, revenue assurance professionals have mourned the loss of “eyes and ears in the field.”

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Even with 95 percent of the utility’s meters automated, “we can get leads from meter readers, cut on/cut off personnel, troubleshooters—anyone out in the field,” said Ron Jones, residential meter services manager for JEA, a utility with more than 360,000 water and electric customers in the Jacksonville, Fla., area. Leads also come from customers, neighbors, jealous exes and “brothers-in-law who talk too much,” he joked.

That’s the time-honored way of finding electricity theft.

Still, Jones admitted, “The old-fashioned methods are dwindling.” Today, only about 30 percent of the utility’s revenue protection leads come from tipsters. The utility relies “more and more on its automated systems,” he said.

Taking Exception

To turn AMI data into leads, many revenue protection teams are tapping the automation features of meter data management systems, which provide “automated exception processing.” An exception is when the system sees an event or data circumstance that it’s not expecting. Examples with revenue-assurance relevance include meter readings that show lower consumption than expected, meters that don’t report any consumption, and readings that show power being used at a supposedly vacant premise.

That can happen when JEA does “virtual” service cut-offs, explained Normen Dailey, director of meter services operations and maintenance. “We don’t roll a truck on a move out,” he said. “We take a reading” through the utility’s AMI system, then send a final bill, leaving the premises ready for the next resident. Since October 2006, the virtual disconnections have saved JEA nearly $700,000 in service-call expenses.

The problem? Such automation leaves a utility open to people who move into a house or apartment but never call the utility to set up an official account. So, managers set consumption thresholds for both electricity and water use. Once those thresholds are surpassed, the meter data management system automatically generates an order for a field-service technician to shut off service.

“We’ve set the parameters for very little loss before we go out” to check on accounts, Dailey said. Prior to putting in an advanced metering system, it took the utility a minimum of 30 days to find active accounts with no contract. Now, revenue protection managers have Dailey’s meter readings to find the no-pays.

The Full Report

JEA’s team also follows up on “plus or minus 20″ reports, in which they especially look at accounts where consumption has gone down by at least 20 percent. The system reviews data over a 13-month period, ensuring the information reflects seasonal usage patterns, Ron Jones explained.

Similar reports are in use at United Illuminating, a wires-only company with 320,000 customers in southwest Connecticut. That utility has reports weighted from highest-usage offenders to lesser larceny, so investigators can prioritize workload.

At Philadelphia-based PECO, reports look for unusual usage patterns, such as usage that drops off substantially on weekends. Through the meter data management system, utility managers compare unusual usage reports with power outage and restoration reports, which whittles down dead-end leads.

In addition, PECO’s managers have created:

  • An “unplanned outage” report that spotlights accounts with more than 10 outages in 30 days. About 40 percent of PECO’s theft detection stems from this report.
  • A “billing window” report to detect meters turned on or off close to the billing period, indicating attempts to force low-balled estimates or pay for only a few days’ worth of consumption. This report pinpoints around 35 percent of the utility’s theft.
  • A “reversed meter” report, which finds power-out and power-up messages that occur in quick succession if the customer unplugs the meter, then plugs it in upside down to make the register run backward. About 20 percent of PECO’s theft shows up via this report.

Along with regular reports, utilities can hire out analytic services, such as those provided by Detectent (www.detectent.com), a provider of software-based revenue protection services, on an ad hoc basis. That’s what JEA did last year and, in November, Detectent gave JEA 80 leads to explore. Some 53 percent of those leads revealed problems. Most came from malfunctions or record-keeping errors, but two cases proved to be outright theft.

Dead meters accounted for 34 of the cases. They weren’t caught by the utility’s “no consumption” report because they occurred on the 5 percent of meters that have yet to be converted to automated metering. Simple code modifications in the meter data management system will prevent such losses from occurring again.

Still, with 73 of the 80 leads now closed, JEA has recovered losses that would equal $205,000 annually. That’s more than twice what the utility paid Detectent for the leads.

Revenue Protection Bolsters AMI Business Case in Honduras

When managers at the Honduran electric utility Empresa Nacional de Energía Eléctrica (ENEE) flipped the switch on a new advanced metering system last May, you could practically hear the utility’s cash register start to “cha-ching.”

ENEE had already saved $1.2 million in U.S dollars by finding theft and other problems during site visits prior to deployment of some 35,000 smart meters. By October, ENEE managers had collected another $368,000 through alerts provided directly from the new metering system serving customers in San Pedro Sula, Honduras. Those alerts let utility officials know when someone was tampering with meters themselves.

But the big bucks rolled in after the utility team started crunching data provided by those new meters. “ENEE recovered about $1.2 million U.S. dollars in the first few days after deployment,” said Jacobo Da Costa, general manager of ENEE’s Northwest region.

Within seven months, the utility had recovered some $21 million in non-technical losses, which are those not included in line loss and other system inefficiencies. Ultimately, utility executives anticipate savings of $24 million during the first year of the advanced metering system’s operation.

According to Da Costa, many houses were hard-wired to steal electricity from the moment they were built. To find those illegal taps, the utility mounted smart meters on transformers and built a dedicated facility for data analysis of consumption information from both customer sites and the transformers serving them. Most of the losses were on the illegally hard-wired accounts, Da Costa said. He added that they would not have been found without the load-balancing system ENEE personnel put in place.

A New Tool

ENEE’s experience is right in line with what has happened for many utilities adopting AMI. Although the systems provide tampering alerts, it is the data, not the alerts, contributing to bottom-line results.

In fact, the alerts often are false. “Those flags were really designed to detect problems with the meter,” explained Wayne Willis, vice president of Detectent. And, although alerts have been repositioned as revenue-protection tools, he says they fall short in that capacity.

For instance, one common flag detects tilts in the meter, which might occur if a would-be thief removed or tampered with the device. Willis says such tilt alerts generally are mercury switches that may jiggle as a result of any strong vibration. Even the passage of heavy trucks has been known to trigger these flags, according to some revenue protection professionals.

Power-down flags have limited value, too. Contractors and utility personnel set them off, Willis said. So do temporary outages. It’s little wonder the Detectent team believes as much as 95 percent of “tamper” flags are erroneous.

However, data analysis is another game altogether. Not only does it help utilities catch energy diversion, it helps cut administrative losses, too. “About 80 percent of losses we help utilities find are not related to theft,” Willis said.

Rather, losses stem from a variety of slip-ups, including meter malfunction. At one Midwest utility, Detectent’s energy-pattern analysis uncovered a series of cases that pointed to a built-in meter glitch. After several accounts showed similar problems, utility staff went out, pulled 1,000 meters from the field and found a consistent corrosion problem inside the units. In that case, the data pointed to “a defect in the meters” themselves, Willis recalled. “The utility was losing about $1.5 million a year on those meters.”

Incorrect meter multipliers are another issue that shows up with smart analysis. Often the meter sees only a percentage of the energy flowing to the customer. For example, suppose a meter registers 1/400th of the consumption, so the meter reading should be multiplied by 400 in the billing software to determine the correct billing amount.

If the multiplier is off, a customer may be paying only 40 times the reading, instead of 400. “Believe it or not, some customers only pay one time what the meter reads,” Willis said. “Those are some of our biggest cases. We see customers who should be paying $30,000 a month, but they’re only paying $300.”

To find such losses, Detectent’s analytics compare consumption data to other pieces of information, such as consumption patterns, business type, consumption of other commodities and more.

Companies like Dectectent aim to help utilities find likely suspects to investigate, but the investigation is still up to the utilities. Data merely identifies the anomalies. It takes people to verify trouble, then collect the money that’s due.

Betsy Loeff has been freelancing for the past 15 years from her home in Golden, Colo. She has been covering utilities for almost four years as a contributor to AMRA News, the monthly publication of the Automatic Meter Reading Association.

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