AI and ML set to modernise India's electricity distribution
Artificial intelligence and machine learning are moving from pilots to everyday operations across India's utilities. At a national conference in Bharat Mandapam, Union minister Manohar Lal called out how these tools can bring intelligent, consumer-centric and self-optimising networks within reach.
His message was simple: use data, automate decisions, and build trust with citizens. Do that, and the grid becomes more reliable, more efficient, and easier to manage at scale.
What this means for utilities and public administrators
- Smart meter analytics for revenue protection and billing accuracy
- Digital twins for planning, asset health, and scenario testing
- Predictive maintenance to reduce equipment failures and truck rolls
- Theft detection to tackle commercial losses
- Appliance-level insights to help households manage usage
- Outage prediction for faster restoration and better crew dispatch
- GenAI decision support to help operators, call centres, and field teams
For citizens, the upside is clear: fewer outages, fairer bills, and clear visibility into consumption. For state budgets, these tools can lower electricity procurement costs and guide smarter investments in infrastructure.
Collaboration, trust, and data foundations
Manohar Lal urged DISCOMs, technology partners, innovators, and academia to work as one team. He also flagged a real risk: misinformation can slow adoption; proactive public communication is non-negotiable.
Power Secretary Pankaj Agarwal reaffirmed the Ministry's focus on digitalisation across DISCOMs. He highlighted three priorities for scaling solutions nationwide: capacity building, secure data-sharing frameworks, and interoperability.
Innovation challenge: winners and use cases
The conference's national innovation challenge drew 195 entries from DISCOMs, AMISPs (Advanced Metering Infrastructure Service Providers), technology firms, and home automation companies. After multiple evaluation rounds, the following teams were recognised:
- DISCOMs: TNPDCL (Tamil Nadu), MP East (Madhya Pradesh)
- AMISPs: Tata Power, Apraava
- Solution providers: Pravah, Flock Energy
- Home automation: Tata Power
Use cases ranged from revenue protection via smart meter analytics to behavioural demand response and unified grid intelligence. The takeaway: practical AI is already delivering measurable results.
New tools and references for planners
The Minister launched STELLAR, a resource adequacy planning tool from the Central Electricity Authority (CEA), to help DISCOMs build long-term load adequacy plans. For leaders tasked with planning and regulation, this adds a structured way to stress-test demand and supply scenarios.
The India Smart Grid Forum (ISGF) also released a handbook covering AI, ML, AR/VR, and robotics-based utility solutions. It features 174 use cases, including 45 from Indian utilities-useful reading for anyone drafting roadmaps or RFPs. See CEA here and ISGF here.
Next steps for DISCOMs and education leaders
- Baseline your data: ensure smart meter rollout, data quality, and unified data models.
- Start focused pilots: theft analytics, outage prediction, and asset health monitoring.
- Set up data governance: secure sharing, consent frameworks, and vendor interoperability.
- Build skills: train engineers, planners, and call centre teams on applied AI.
- Measure impact: track loss reduction, SAIDI/SAIFI, billing accuracy, and customer satisfaction.
- Plan for scale: standardise APIs and procurement to avoid vendor lock-in.
If you're building internal AI capability, curated learning paths can speed things up. Explore role-based options at Complete AI Training or browse the latest AI courses.
Your membership also unlocks:
