Machine Learning Helps Power Down Electricity Theft in Jamaica

 § In Jamaica, about a quarter of electricity produced is “lost” through technical losses, non-paying customers and/or accounting errors. Manual detection has failed to make a difference in reducing this theft.

§ Together with Impact Lab and UChicago, we used predictive analytics and machine learning to help Jamaican utility JPS identify and decrease incidents of theft.

§ The model is based on 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.

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. 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.

 Now, Jamaica is one of the first countries to use machine learning to tackle its electricity theft problem. The World Bank partnered with Chicago-based data science firm The Impact Lab, and the Energy Sector Management Assistance Program (ESMAP) to use machine learning to improve JPS’ theft identification process among large and commercial accounts.

Tom Plagge, Co-founder and Chief Scientist of The Impact Lab shared, “Machine learning models oftentimes feel like magic, so we showed Jamaican utility staff how these results build on the company’s staff intuition, fit with what they already know through their experiences. It’s like doing what you’re already doing but in an automated, more precise and faster way.”

The project helped JPS to combine machine learning and human intelligence to produce a digital prototype model. Manual theft detection was replaced by time series visualizations, heat maps of usage, and detailed phase information for each account. Armed with this information, the strike rate of successful JPS investigations increased substantially, during the first months of its implementation the strike rate had doubled.

The developed and tested tool is now housed on GitHub’s development platform. Its code is freely useable and ready to be plugged into the accounts’ data of any utility in a situation when a company has a similar problem.

For more information, contact Anna Lerner (alerner@worldbank.org).