Today’s Energy Theft Detection Models Help Protect Revenues While Enhancing Neighborhood Safety

July 2010 Vol. 237 No. 7

Michael Madrazo, CEO, Detectent

People who turn valves that only the utility should operate, or who install unauthorized connections to divert gas around meters and into their homes or businesses are not only breaking the law, they are also endangering property and lives. Tampering with a gas meter or natural gas line could cause property damage and fatal injury from asphyxiation, explosion or fire.

The danger exists not just to the person who did the tampering but also to the structure and its occupants, neighboring premises and to utility and service personnel.

Unauthorized use of energy affects all customers through higher costs. Each year, usage without the utilities’ knowledge through meters that have been bypassed or tampered with result in the loss of billions of dollars worth of lost energy. That can mean higher prices for trusted customers as utilities try to recover revenue for energy that was used but never reported.

The following should motivate LDCs to identify and shutoff gas thieves: (1) public safety, (2) lost revenues, (3) desire to reduce consumer billing rates, (4) community responsibility, (5) desire for more accurate information baselines for local distribution companies’ (LDCs) rate cases to state public utility commissions (PUCs), (6) liability and (7) gas emissions standards.

Traditionally, utilities have relied on meter readers and service personnel for tips on gas theft. Meter readers, visiting each meter every month to collect a reading, made for a very effective line of defense against theft. However, with the advent of automatic meter reading (AMR) and advanced meter infrastructure (AMI), the meter reader is being replaced with technology and the utility has lost its eyes and ears in the field.

AMR and AMI systems provide tamper flags that identify energy theft when the tampering occurs at the meter, and while they can alert the utility to a theft case, they also can trigger far too many false alarms. According to a Chartwell research report, “AMR is not a perfect system for detecting theft and many customers have discovered this, coming up with clever ways to bypass or tamper with the meter without triggering a tamper flag. Plus, some of the utilities consulted for this report say tamper flags do not always indicate theft, but are a product of oversensitivity and benign outside forces.”

The only way to identify theft in this situation is to clearly understand how each customer should use natural gas and focus on those who deviate from expected consumption.

Knowing The Customer
Knowing the customer is vital. Typically, utility data consists of the account holder’s name, phone number, address and–for commercial accounts–type of business and maybe the name of the business. The utility may know a client account is a restaurant, for example, but probably doesn’t know how large the premise is or whether it is a sit-down or take-out establishment. For commercial accounts, additional information such as the number of employees, chain affiliation and other metered services on the account need to be a part of any intelligent usage analysis. For residential accounts, knowledge of the size of premise and heating sources is also very valuable.

Understanding the characteristics of an account provides valuable insight into the customer’s use of gas. With this insight, energy usage can be compared to a group of “like peers” with similar characteristics and outliers can be flagged for further analysis and investigation.

The process of detecting theft analytically can only be accomplished once multitudes of characteristics of an account are identified and proper peer grouping is done. Useful data for analysis includes: (1) usage from other meters for the same service type on the account, (2) usage from other metered services, (3) correct business codes, (4) square footage, and (5) business information such as number of employees and hours of operation.

Comparisons And Characteristics
Utility customer data can be significantly enhanced and improved by acquiring third-party data and integrating it using sophisticated pattern matching algorithms. Correct business codes as well as a myriad of additional premises and operational information can be gathered and used to dramatically extend the known information for each customer account.

Theft detection models generally fall into two categories: peer comparison and characteristic analysis.

Peer comparison models contrast all available information about residential and commercial customers to similar homes and businesses within similar geographical and environmental settings. Deviation from expected usage can indicate that not all the energy used by a customer is being metered correctly.

Characteristic analysis looks for anomalies in a consumption pattern that might be indicative of un-metered equipment in an account. For example, you can expect a laundromat to have a relat¬ionship between electricity consumed by washing machines and gas consumed by dryers. If one service is not metered to the expected ratio of the other, then it may be indicative of one of the services not being metered correctly or having been tampered with. When combined, these two types of theft detection models can monitor for adherence to peer usage and micro-analyze energy usage for expected characteristics. Case In Point
Detectent, of the San Diego, CA, area, works with North American utilities to detect gas theft by using proven analytic methods and systems. Recently, the company helped a utility find a bypass at a commercial account by comparing the gas usage at that account to that of similar commercial accounts.

The customer had installed a bypass upstream of the gas meter and concealed the bypass behind a false wall. Over a period of two years, the proprietor of the business had gradually closed down a valve leading to the meter and directed more gas around the meter through the installed bypass piping. The gradual shunting was done so as to not set-off any high/low alarms that the utility might have and the customer continued to allow some amount of gas to flow through the meter so that there was never a zero-usage reading.

Only through knowing more about the customer and through the application of peer analysis was this theft of service detected and ultimately stopped – and the customer charged for the ill-gotten service.

Using The Tools
Analysis on its own does not replace the need for the common sense and intuition that people bring to an equation. So even with all of the known data captured and analyzed, a review of all available information needs to be done in order to confirm that the indications of energy theft are not in fact simply the work of other outside forces. For example, one needs to consider if demographically, the area has declined, or if the business is going through renovation or other legitimate changes that might lead to a deviation from the expected normal usage.

Unlike simpler query tools, the gas theft detection solutions that have emerged in recent years have the capability to organize cases in order of value and probability so that both back office and field resources are used most efficiently. For commercial accounts, this might be accomplished by determining: (1) average consumption compared to the capacity of the installed meter(s), (2) ratio of one service’s consumption to that of another, and (3) degree of deviation from expected normal values.

Using many data sources and a combination of models that look for independent features in a customer’s consumption profile have transformed theft detection into a viable and cost-effective solution for utilities. Previous attempts to analytically identify energy and gas theft resulted in marginal improvements over past practices and typically were not cost effective. The newer solutions, which have emerged only in the past three to five years, have significantly increased in cost effectiveness.

Compare, Contrast And Learn
With an analytical approach to gas theft, it is important to know that no one analytical model stands alone. Numerous models need to run in parallel in order to evaluate an account’s energy usage from a variety of angles and flag anomalies based on different forms of assessment. Today’s gas theft detection systems do just that and therefore go well beyond the utility’s traditional high/low and zero-consumption reports.

Deploying a theft detection solution is not only important to the future of a utility with an AMI or AMR deployment; it is also critical to traditional utility operations. Economic conditions have caused utility customers – both residential and commercial – to act in ways and do things that they have not done in the past. All indications are that gas theft is rising rapidly across the country. Moreover, with revenues dropping from reductions in industrial energy and gas usage, remediating theft can be a valuable revenue stream for the utility.

As with the restaurant and laundromat examples, only by truly understanding the customer and how he or she uses gas can utilities expect to detect theft and other forms of revenue loss. Utilizing sophisticated customer intelligence tools, today’s utilities can identify and reduce revenue losses by analytically and proactively protecting their delivery networks from tampering, malfunctions and theft.

The Author
Michael Madrazo is the CEO, founder and president of Detectent, a pioneer in developing analytical revenue protection solutions. 760-233-4030, Email:,