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Business Wire India

Global technology service provider, Bidgely, has launched an Energy Theft Solution that leverages the power of AI-enabled data analytics to detect and resolve India’s energy misuse challenge.

A brainchild of the data scientists at its Bangalore engineering center, this solution by Bidgely reaffirms its commitment to Atmanirbhar Bharat by solving the country's energy theft problem. The Government of India has been rapidly adopting smart meters and the solution unlocks AI-powered insights from meter data. 
 
Bidgely has been selected by REC Ltd, (formerly Rural Electrification Corporation), an undertaking of the Government of India, to participate in the Ministry of Power, GoI Technology Incubation Challenge Powerthon 2022, which was organized in collaboration with SINE incubation lab of IIT Bombay. From among 200 applicants and 18 ultimate technology solution providers chosen to participate, Bidgely was the only provider selected for energy theft detection in support of the national Revamped Distribution Sector Scheme (RDSS) to reduce losses across India up to 15 percent by 2025.
 
Bidgely's newly launched Energy Theft Solution will demonstrate how AI-enabled data analytics can detect and resolve India’s energy misuse issues during a 4-month engagement with a state utility. Congruently, Bidgely is working with a large utility in central India – in collaboration with the World Bank – to further prove theft detection analytics use cases.
 
“High levels of technical and commercial energy losses are a major financial concern for state utilities – with 22 percent of distributed power going unaccounted for – and energy theft is one of the largest contributors. As India deploys hundreds of millions of smart meters by 2030, leveraging capabilities in artificial intelligence and machine learning to this new wave of energy data will be critical to support the objectives of the Ministry of Power,” said Sh R Lakshmanan, CEO of REC Power Development and Consultancy Limited, a wholly owned subsidiary of REC Limited.
 
With 17 patents and over 40 global energy customers, Bidgely is extending its expertise in energy analytics to India’s Powerthon 2022 energy theft challenge – enabling state utilities to not only understand energy loss with precision and accuracy, but also insights into how to prevent further loss.
 
“Bidgely is keenly aware of the challenges in India’s energy market through engagement with key stakeholders and with a large portion of our workforce operating from the region, including both our data science and research and development teams,” said Gautam Aggarwal, Chief Revenue Officer of Bidgely. “We are delighted to be able to demonstrate the power of behind the meter intelligence for bringing about improvements in the billing efficiency of Discoms in India.”
 
Bidgely Energy Theft Solution
 
Based on industry-leading non-intrusive load monitoring technology and advanced data analytics, Bidgely’s Energy Theft Solution disaggregates customers’ energy consumption down to the appliance level. Insights generated from this data are used to identify usage patterns over a period of time and detect anomalies associated with high probability theft tendencies. Bidgely’s solution addresses three specific energy theft use cases: 
 
  • Tariff misuse – when a customer’s electricity use type is mischaracterized and an incorrect lower tariff is applied, Bidgely accurately distinguishes between residential behavior and commercial behavior as well as a transition between the two.
  • Meter tampering – Bidgely leverages energy consumption patterns, technical parameters and available smart meter events to identify premises where the meter itself has been tampered.
  • Direct theft – in cases when the meter is passed partially or completely to prevent consumption from registering, Bidgely identifies which appliances are running and at what times to determine if the behavior is anomalous.
 
To learn more about Bidgely’s Energy Theft Solution for detecting energy misuse, visit bidgely.com/solutions/energy-theft-detection/.


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