In Jamaica, about a quarter of electricity produced is stolen or “lost” through non-paying customers and/or accounting errors. Manual detection has failed to make a difference in reducing this theft.
ESMAP’s technical assistance team implemented a machine learning model to help Jamaican utility JPS identify and decrease incidents of theft.
The machine learning model is based on an open source code, and is available for free to any utility.
About a quarter of the electricity produced by Jamaica’s energy utility, Jamaica Public Service (JPS) is stolen. When traditional, labor-intensive methods failed to produce lasting results, Jamaica tried a different approach: machine learning.
Globally, billions of dollars are lost every year due to electricity theft
, wherein electricity is distributed to customers but is never paid for. In 2014 alone, Jamaica’s total power transmission and distribution system reported
27% of losses (due to technical and non-technical reasons), close to double the regional average. While the utility company absorbs a portion of the cost, it also passes some of that cost onto consumers. Both actors therefore have an incentive to want to change this.
To combat this, JPS would spend more than $10 million (USD)
on anti-theft measures every year, only to see theft numbers temporarily dip before climbing back up again. The problem was, these measures relied primarily on human-intensive, manual detection, and customers stealing electricity used more and more sophisticated ways to go around regularly metered use. JPS employees would use their institutional knowledge of serial offenders and would spend hours poring over metering data to uncover irregular patterns in electricity usage to identify shady accounts. But it wasn’t enough to effectively quash incidents of theft.