How Artificial Intelligence is Powering India’s Renewable Revolution

As BESS and green-hydrogen projects expand, AI is becoming the conductor that synchronises them. Algorithms decide when to charge, discharge or operate electrolysers by weighing weather, price and grid constraints.

November 19, 2025. By News Bureau

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articles

Dr. Yogesh Kumar K

Alliance University

Green hydrogen projects

BESS

Wind-swept coasts

artificial intelligence

International Renewable Energy Agency (IRENA)

Clean-Energy Framework

In the sunlit deserts of Rajasthan, the wind-swept coasts of Tamil Nadu and the vast solar parks of Gujarat, India’s energy landscape is undergoing a profound transformation. As of September 2025, the country has crossed 247 GW of renewable capacity—127 GW of solar, 53 GW of wind, and 50 GW of large hydro—placing it among the world’s leading clean-energy nations. Yet beyond these numbers lies a deeper shift powered not by new turbines or panels but by algorithms. Artificial intelligence (AI) has become the invisible architecture that turns variability into predictability, data into decisions, and renewable energy into a reliable backbone of India’s sustainable growth.

Globally, the renewable revolution is accelerating. The International Renewable Energy Agency (IRENA) reports that the world added around 585 GW of renewable capacity in 2024, pushing the total beyond 4.4 TW, with solar and wind dominating new additions. The challenge is no longer just generation—it is integration: balancing fluctuating supply with dynamic demand, maintaining grid stability, and keeping energy affordable. AI has emerged as the operational brain of this new ecosystem, converting terabytes of data from sensors, satellites and smart meters into predictive insights that make renewable power dispatchable and dependable.

Across the world, AI is becoming the differentiator. China uses deep-learning models to coordinate over 100 GW of hybrid solar–wind parks in Inner Mongolia and Gansu. The United States employs AI-based grid controllers to balance battery storage and renewable generation in California and Texas. The European Union’s AI4Energy and Digital Twin programmes simulate power flows across the continent, while Japan and South Korea deploy AI for real-time industrial and household demand management. India, now the fourth-largest renewable-energy market, is advancing rapidly in this direction. Its advantage lies in building a digital-ready grid from inception rather than retrofitting outdated infrastructure—embedding intelligence where it matters most.

AI at the Heart of the Energy Revolution

India’s Renewable Energy Management Centres (REMCs) form the analytical core of this transformation. They integrate numerical weather prediction, satellite imagery and real-time telemetry using machine-learning architectures such as gradient-boosted regression models and recurrent neural networks. These systems have improved day-ahead forecast accuracy by nearly 20 per cent, cutting deviation penalties and reserve requirements. By predicting wind and solar output with far greater precision, AI has converted intermittent sources into manageable assets.

The same intelligence is optimising operations on the ground. In the wind corridors of Gujarat and Tamil Nadu, sensors monitor vibration and torque data, allowing AI algorithms to detect mechanical wear long before failure. In solar parks, drones equipped with thermal cameras and computer vision identify hotspots, cracks and dust accumulation that human inspection could miss. Predictive maintenance recovers 3–5 per cent of lost generation annually while reducing downtime by nearly a third—an enormous boost to asset reliability and return on investment.

AI’s impact extends to the distribution network, where electricity meets consumers. Under the Revamped Distribution Sector Scheme (RDSS), over 20 crore smart meters have been sanctioned, with 2.4 crore already installed. The resulting data streams allow utilities to identify power theft, transformer stress and voltage imbalances in real time. Tata Power-DDL has reduced its aggregate technical and commercial losses to around five per cent—among the lowest in the country—through analytics and automated fault localisation. Automated demand-response systems, meanwhile, use AI to orchestrate industrial and commercial loads that can shift consumption patterns when solar power peaks, creating a city-scale “virtual power plant” that balances the grid dynamically.

As battery-energy-storage systems (BESS) and green-hydrogen projects expand, AI is becoming the conductor that synchronises them. Algorithms decide when to charge, discharge or operate electrolysers by weighing weather, price and grid constraints. The National Green Hydrogen Mission, with an investment of INR 19,744 crore, explicitly encourages AI-based optimisation to lower production costs and improve system efficiency. Internationally, firms such as Siemens Energy, ABB, AutoGrid and Tesla Energy are leading similar orchestration models, several of which are now being adapted to Indian hybrid projects. Together, they mark the rise of a grid that not only delivers energy but continuously learns from it.

Powering the Future: India’s Intelligent Clean-Energy Framework

India’s policy ecosystem increasingly rewards intelligence alongside capacity. MNRE and SECI hybrid and round-the-clock tenders prioritise accurate forecasting and optimised storage. The RDSS continues to provide the data backbone for analytics, while new wind data-localisation norms enhance security and interoperability. Under Digital India, upcoming AI Centres of Excellence aim to cultivate domestic talent in energy informatics and cybersecurity.

Industry momentum is equally strong. Adani Green Energy, ReNew Power, NTPC Green, Suzlon, GE Vernova India and Goldi Solar are embedding AI across forecasting, manufacturing and operations. PGCIL and several State Load Dispatch Centres are experimenting with AI-assisted control rooms and digital-twin simulations to model grid behaviour. This collaboration among government, industry and academia is laying the foundation for a truly intelligent clean-energy ecosystem.

The benefits are measurable. Even a two-to-three per cent improvement in utilisation efficiency through better forecasting, predictive maintenance and demand flexibility could yield an additional 10–12 terawatt-hours (TWh) of clean electricity every year—enough to power a medium-sized Indian city. Yet challenges persist: fragmented data, limited interoperability, and growing cybersecurity threats demand urgent attention. AI’s own energy footprint is also rising; data-centre demand may exceed 4.5 GW by 2030, making renewable-powered computing essential to maintain net-zero integrity.

India’s target of 500 GW of non-fossil capacity by 2030 will depend as much on intelligence as on infrastructure. The deserts, coasts and plateaus will continue to generate power, but algorithms will determine how efficiently that power is stored, routed and consumed. AI is already forecasting cloud movements, diagnosing faults before failure, and balancing millions of distributed loads in real time. If India sustains its current momentum in innovation, policy and investment, it will not merely meet its domestic energy goals—it will define a global template for intelligent decarbonisation, where progress is measured not only in gigawatts installed, but in smart watts delivered.

- Dr. Yogesh Kumar K, Professor & Research Faculty, Alliance School of Sciences, Alliance University