Combating Energy Theft with Actionable Intelligence

Combating Energy Theft with Actionable Intelligence

According to Northeast Group, energy suppliers are losing $96B each year to energy theft. To put that in context, it is roughly equivalent to the total goal for climate action financing by developing countries in 2020. Although often perceived as a developing nation issue, the problem is widespread and impacts every geographical region. Creative thieves use a variety of methods to siphon energy including direct line tapping, magnetic interference, and bypassing the electricity meter. Given the size of the problem, a variety of methods have been developed to detect theft attempts and inform the energy supplier so that appropriate action can be taken. So far, results have been unsatisfactory and energy theft continues to rise. How can we reverse this trend?

The root cause of the problem is that each tamper detection method has weaknesses. The insights and alerts generated are prone to error, leading to a lack of trust in the solution. They provide interesting views on the problem, but they don’t provide real-time actionable intelligence.

The most pervasive method in use today is pattern-based analytics with machine learning to identify anomalies and profile tamper candidates. Meter-based historical and neighbor data are combined with other sources and mined for patterns that deviate from an expected norm. Anomalies can be priority ranked, and in theory, offenders caught. In practice though, this method tends to deliver an amount of false positives, (i.e. results that are profiled as tampers but are actually not). For example, a homeowner goes on extended work assignment leaving the property unoccupied for a few months. Power consumption drops and a tamper candidate alert is triggered, leading the energy provider to initiate an erroneous investigation with a resulting waste of resources, frustrated homeowner, and damaged reputation. Another problem is that, by definition, the analysis relies on                                                                       historical data and lags the actual theft.

Another common method of tamper detection is meter-hardware protection. Basic meters contain built-in detectors that are tripped by certain kinds of tamper attempts and then alert the energy supplier. Anecdotal feedback from utilities deploying these detectors indicates that, generally, such systems are over-sensitive and also prone to the false positive problem. In short, the alerts cannot be acted upon because in a high number of cases the alerts are triggered innocently.

Recent innovations have taken a more holistic, grid intelligence or network based approach. Energy consumption is measured at multiple points in the energy distribution chain, results are compared, and any differences are attributed to technical or non-technical (i.e. theft) loses. Such solutions show promise, but the granularity of the results is wide-ranging. It is simply not economical to measure consumption at all the network points needed to profile a theft to a specific end node.


All existing methods also suffer from one core flaw. While they can, to a greater or lesser extent, point to a potential tamper, they cannot reliably indicate the amount of energy stolen.

A new approach is needed, providing on-meter, continuous real-time monitoring with an associated analytics capability that can profile, quantify, and alert energy suppliers to tamper attempts. This approach must deliver consistent and reliable results that allow action to be taken with high confidence.

That is where mSureRegistered comes in. mSure is an agent that resides in the smart meter and monitors what happens at the sensor used to detect energy consumption. Any change to the characteristics of the sensor that would be induced by an attempt to bypass or saturate the meter can be immediately detected. That enables mSure to send the energy provider a tamper alert and/or to activate a visual flag at the meter, which can act as a deterrent to potential tampering. As the impact of various direct tamper methods on the sensor can be profiled, the type of tamper can be recognized with high confidence and the number of false positives significantly reduced. In addition, by understanding and analyzing the change in characteristics, an estimate can be made of the amount of energy stolen, not just that a tamper event has occurred.

While a fool-proof revenue protection solution will likely embrace a combination of methods, mSure provides the missing piece of the puzzle: a meter-level real-time tamper detection capability that can be acted on with confidence. In short, actionable intelligence.


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