Ministry of Power Recognises Apraava Energy’s AI-ML Solution for Power Distribution Advancement
Apraava Energy has received national recognition for its AI and ML solution at the Ministry of Power’s National Conference under the RDSS, earning the runner-up award for advancing data-driven, consumer-centric power distribution, presented by Union Minister Manohar Lal Khattar.
December 16, 2025. By News Bureau
Nearly 200 solutions were presented by the DISCOMs, Advanced Metering Infrastructure Service Providers (AMISPs), Technology Solution Providers (TSPs) and Home Automation Solution Providers (HASPs).
Under the AMISP category, Apraava Energy showcased its solution Discom GPT (Grid.), developed in partnership with WorkOnGrid. The next-generation solution demonstrated how behavioural demand response and AI-based operational automation can enhance consumer experience while improving power grid efficiency. By integrating generative AI, advanced ML and a robust data architecture, Grid. unlocks new levels of value, efficiency and reliability in the power distribution sector, setting the course for smarter decisions, agile responses and transformative change across India’s evolving digital energy landscape.
Rajiv Ranjan Mishra, Managing Director, Apraava Energy, said, “Grid. represents Apraava’s effort to reimagine the future of power distribution for India. It is not merely a technology upgrade, but a bold step towards embedding AI and Machine Learning into power utility operations. Through this initiative, our mission is to improve grid reliability, operational efficiency, and customer satisfaction. We are delighted by the recognition and appreciation received from the Ministry of Power for our solution. Apraava remains committed to delivering reliable and affordable energy solutions to the nation, with a focus on innovation and sustainability.”
Through Grid., Apraava demonstrated how utilities can segment consumers to enable targeted interventions and effective demand-side management, predict transformer faults using anomaly detection to support proactive maintenance, forecast demand more accurately through deep learning models for improved load planning, and automate operational workflows to deliver real-time insights and faster decision-making support.
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