Adani Electricity champions customer interests with machine learning tech for fair power
Adani Electricity on Wednesday said it has strengthened its commitment to delivering fair and reliable power by deploying advanced theft prediction and revenue protection modules based on Machine Learning (ML) and meter data technologies across its distribution network.
The initiative is designed to curb electricity theft, protect genuine customers, and enhance governance for a transparent and efficient power ecosystem.
The company rolled out a Machine Learning-based theft prediction module in January.
Since then, “it has detected electricity theft totalling 5.0 million units (MUs), valued at Rs 8.59 crore,” Adani Electricity said in a statement.
In a recent high-value case, the technology uncovered a direct supply theft at an electroplating unit in Malad (W), involving 0.4 MU worth Rs 87 lakh. These advanced tools enable swift, data-driven action, ensuring fairness and shielding honest consumers from the burden of illegal usage, the company said.
Vigilance efforts have been strategically focused on high-risk areas, guided by surveillance and credible intelligence, while the Machine Learning module integration has reinforced governance through comprehensive theft analysis.
“We are committed to leveraging advanced technologies to ensure a reliable and secure power supply,” an Adani Electricity spokesperson said.
"The integration of machine learning has enhanced theft detection, strengthened governance, and protected genuine customers from the impact of illegal usage -- reflecting our vision for a smarter, sustainable energy future," the spokesperson added.
The Machine Learning module-powered system automates data analysis, detects pattern-based anomalies, and accelerates theft identification.
By analysing customer profiles and consumption patterns, it accurately flags potential cases, enabling faster response times, targeted inspections, and informed decision-making.
This data-driven approach not only strengthens enforcement but also reduces operational costs, ensuring fairness and reliability for consumers, the company said.
