Empowering India’s Energy Transition with AI

This editorial is based on “AI’s role in India’s energy transition, a reality check needed” which was published in The Hindu on 12/07/2025. The article brings into picture the role of AI in addressing India’s doubling energy demand by 2030 through improved forecasting, grid efficiency, and reduced transmission losses, while highlighting the need for policy, investment, and inclusive engagement.

For Prelims: India's renewable energy capacity, Carbon footprints, Smart metering, Revamped Distribution Sector Scheme, International Energy Agency, SDG 7 (Affordable and Clean Energy).  

For Mains: Role of AI in India’s Energy Transition, Key Issues Associated with AI Integration in Energy Transition 

India's energy demand is set to double by 2030, pushing the nation toward ambitious targets of 500 GW non-fossil fuel capacity and net-zero by 2070. Artificial Intelligence emerges as a transformative tool for this transition, offering solutions for renewable energy forecasting, grid optimization, and reducing the sector's substantial 20-30% transmission losses. Success in India's AI-driven energy transition requires collaborative efforts between government policy support, private investment, and community engagement to ensure benefits reach both urban and rural areas equitably. 

What Role can Artificial Intelligence Play in India’s Energy Transition?  

• Enhancing Grid Operations and Renewable Energy Integration: AI is crucial for optimizing grid operations as India scales up renewable energy integration.  
  • With increasing renewable energy capacity, especially solar and wind, their intermittent nature can cause grid instability. AI technologies can predict energy production and demand fluctuations, enabling better grid management and reducing reliance on fossil fuels. 
  • For instance, in a pilot project in Gujarat, AI-driven models reduced the gap between predicted and actual solar power generation by 30%, helping grid operators optimize energy distribution. 
• Improving Energy Efficiency through Demand-Side Management: AI can revolutionize energy efficiency by enabling real-time demand-side management.  
  • This is critical for India’s grid, which is under pressure from a rapidly growing population and industrialization.  
  • AI-driven systems can adjust energy supply based on real-time usage patterns, optimizing consumption and minimizing peak load strain. 
  • For instance, ABB Ability Energy Forecasting uses AI to give facility managers accurate power consumption predictions.  
    • Energy Forecasting enables them to take timely action to reduce unplanned consumption spikes by re-scheduling or switching off non-critical loads 
  • Also,  AI’s predictive maintenance capabilities are vital for reducing the high transmission and distribution (T&D) losses, which reach up to 20% in India.  
• AI-Driven Scaling of Renewable Energy Production: AI can help India scale its renewable energy production by optimizing the performance of solar panels and wind turbines. By analyzing weather patterns and operational data, AI can fine-tune system operations to enhance energy output. 
  • For instance, Machine learning has boosted the value of our wind energy by roughly 20%, compared to the baseline scenario of no time-based commitments to the grid. 
  • Also, Google's AI subsidiary DeepMind has developed a machine learning algorithm to predict the productivity of wind farms up to 36 hours in advance. 
• AI for Climate Impact Mitigation and Carbon Emissions Reduction: As India strives to meet its carbon reduction goals under the Paris Agreement, AI is a key enabler in identifying emission hotspots and developing strategies for minimizing them.  
  • By analyzing vast amounts of environmental data, AI can pinpoint inefficiencies across industries, cities, and energy sectors, offering solutions to reduce carbon footprints. 
  • A recent report from McKinsey reveals that AI-driven technologies can help businesses reduce their CO2 emissions by up to 10% and cut energy costs by 10-20%, significantly aiding India’s net-zero target by 2070. 
• AI for Decentralized Renewable Energy Systems: As India moves towards decentralizing its energy infrastructure, AI can play a crucial role in optimizing distributed renewable energy systems, such as rooftop solar panels, small wind turbines, and microgrids.  
  • AI algorithms can monitor and manage these decentralized energy sources in real time, ensuring optimal energy production, consumption, and storage.  
  • In rural areas, where grid connectivity is limited, AI can help microgrids operate autonomously by analyzing local weather patterns and energy needs.  
    • For example, time is not far when a microgrid in a village in Uttar Pradesh uses AI to automatically adjust energy generation and consumption, ensuring a stable power supply without depending on the main grid. 
• AI in Electric Vehicle (EV) Integration and Charging Networks: The rapid adoption of electric vehicles in India can be significantly enhanced by AI.  
  • AI technologies can optimize the charging infrastructure, ensuring efficient distribution of power to EV charging stations across the country.  
  • AI can predict the best times for EVs to charge based on grid demand, renewable energy availability, and user behavior.  
    • Additionally, AI can aid in balancing the load on the grid by shifting charging times to off-peak hours or during high renewable energy output, reducing the strain on the grid.  
    • For instance, in cities like Bengaluru, AI-based EV charging networks can successfully minimize grid congestion during peak hours , while ensuring that EVs are charged efficiently and sustainably. 

What are the Key Issues Associated with AI Integration in Energy Transition?  

• Data Quality and Availability for AI Training: AI systems heavily rely on large, high-quality datasets to function effectively. However, India’s energy sector faces significant challenges regarding data availability, accuracy, and consistency. Many regions still lack proper data collection mechanisms, limiting AI’s potential in energy management. 
  • Many smart grid and renewable energy projects in rural areas face difficulties in gathering real-time data due to outdated infrastructure. 
  • Out of the 14.8 million Distribution Transformers (DTs) under state power distribution companies (discoms), only 35% have been approved for smart metering.  
    • Also, as per data from the Revamped Distribution Sector Scheme (RDSS) portal, only about 0.44 million DTs (3% of the total) currently have smart meters functioning in communicating mode. 
• Skill Shortage in AI and Data Analytics: The lack of AI and data analytics expertise is a major bottleneck for the effective implementation of AI solutions in India’s energy transition.  
  • As the demand for AI professionals outpaces supply, this skill gap is critical to the success of AI-powered energy solutions. 
  • India’s renewable energy sector has faced challenges in deploying AI effectively due to a lack of professionals who can develop, implement, and maintain AI-based systems. 
    • India is experiencing a significant AI talent shortage, with just one Generative AI (GenAI) engineer available for every 10 open positions, and only 49% of the overall demand currently being met, according to a report. 
• Cybersecurity and Data Privacy Concerns: With AI systems handling sensitive data across energy grids, there are heightened concerns regarding cybersecurity and data privacy.  
  • As more data points are generated by AI-driven smart grids, the risk of cyberattacks grows, potentially compromising the entire energy infrastructure. 
  • For instance, there have already been reports of cyberattacks targeting India's power grid, notably one involving a group called "RedEcho" potentially linked to the Chinese government, highlighting vulnerabilities in the system. 
  • Also, in early 2024, an espionage campaign aimed at the Indian energy sector was uncovered, utilizing modified malware to collect sensitive data 
• Regulatory and Policy Gaps: India’s regulatory framework for AI deployment in the energy sector remains underdeveloped, hindering the full-scale adoption of AI.  
  • As AI technologies evolve faster than regulatory bodies can keep up, there’s a risk of uncoordinated or inefficient AI integration in energy systems. 
  • The Indian government has yet to develop comprehensive guidelines for AI’s role in energy forecasting and grid management, causing uncertainty among stakeholders. 
  • For instance, during FY 2020–21, solar and wind contributed approximately 29% of Karnataka’s annual electricity generation, 20% in Rajasthan, 18% in Tamil Nadu, and 14% in Gujarat. However, these states lack an adequate regulatory framework for the integration of Artificial Intelligence (AI) in the energy sector. 
• Environmental Impact of AI Energy Consumption: AI systems, especially those using deep learning and machine learning models, itself require significant computational power, leading to increased energy consumption.  
  • In a country like India, where energy demand is already rising rapidly, this additional consumption could offset the environmental benefits of AI in energy systems. 
  • Training an AI model for energy forecasting can consume more electricity than some small cities use in a year, raising concerns over AI’s environmental footprint. 
  • A study by the International Energy Agency (IEA) reveals that electricity demand from data centres for AI worldwide is set to more than double by 2030 to around 945 terawatt-hours (TWh), slightly more than the entire electricity consumption of Japan today, a concerning figure for India’s sustainability goals. 
• Integration Challenges with Legacy Infrastructure: India’s existing energy infrastructure is outdated and incompatible with the latest AI technologies, creating significant integration challenges.  
  • AI requires modern, interoperable systems, and without upgrades to legacy grids, many regions will be unable to harness AI’s full potential in energy optimization. 
  • In areas with older grid systems, AI integration faces major hurdles, such as inefficient communication between devices, limiting the effectiveness of AI in energy management. 
  • Currently, India requires an investment of approximately ₹2,442 billion in grid expansion and related infrastructure, which makes AI integration secondary.  

What Measures can India Adopt to Effectively Integrate AI in India's Energy Transition? 

• Create a National AI and Energy Innovation Framework: India should develop a national AI strategy specific to the energy sector, encompassing clear guidelines and an action plan for AI integration across all stages of energy production, storage, distribution, and consumption.  
  • This framework should establish standardized protocols for AI deployment, data sharing, and interoperability among energy players (e.g., utilities, government bodies, and tech firms).  
  • By institutionalizing this framework, India can ensure consistent policy guidance and an innovation-driven AI ecosystem. 
• Leverage AI for Predictive Grid Management and Smart Load Balancing: India’s grid suffers from inefficiencies, outages, and heavy congestion during peak demand.  
  • AI can be used for predictive grid management, utilizing machine learning models to forecast power demand and supply, thus enabling operators to manage grid loads dynamically in real-time.  
  • AI-driven smart load balancing tools can anticipate fluctuations in renewable energy generation (wind/solar) and adjust energy flows accordingly, reducing power disruptions. 
• Establish a Nationwide Smart Meter Rollout with Polit AI Integration: India can fast-track the adoption of smart meters and some equipped with AI capabilities for real-time energy consumption monitoring.  
  • By collecting granular data, AI can optimize consumption patterns, offer predictive maintenance, and manage peak loads more effectively.  
  • Implementing these smart meters across urban and rural areas will provide valuable data for utilities and consumers, helping to reduce transmission losses and energy wastage 
• AI-Driven Renewable Energy Forecasting System: AI can be utilized to create a centralized, cloud-based renewable energy forecasting platform, enabling more accurate predictions of solar, wind, and hydro generation.  
  • The system would use weather data, historical generation patterns, and machine learning models to provide short-term (hourly, daily) and long-term (seasonal) forecasts.  
  • These forecasts can help grid operators optimize energy dispatch and integrate renewables more smoothly, reducing reliance on fossil fuels. 
• Invest in AI-Driven Decentralized Microgrids for Rural Electrification: AI can transform rural electrification by deploying microgrids that utilize AI for real-time energy management, predictive maintenance, and autonomous fault detection.  
  • These AI-powered systems can integrate local renewable sources (e.g., solar, biomass) and store excess energy in batteries for later use.  
  • The use of AI in microgrids allows for the decentralized and sustainable management of energy in remote areas, bypassing the need for extensive grid infrastructure. 
• Private Sector Partnerships to Develop AI-Enabled Smart Energy Solutions: The Indian government should incentivize private companies and startups to develop AI-powered energy solutions, such as smart thermostats, predictive maintenance tools, and energy optimization platforms for industries.  
  • This can be achieved through tax benefits, funding for R&D, and creating innovation hubs in collaboration with universities and tech giants.  
  • Encouraging private-public partnerships will accelerate the scaling of AI-based solutions in diverse areas such as demand-side management, energy efficiency, and green building technologies. 
• Develop AI for Real-Time Energy Theft Detection and Fraud Prevention: AI systems can be employed to detect anomalies and prevent energy theft, which is a significant issue in India, especially in rural and underprivileged urban areas.  
  • AI-powered systems can analyze consumption patterns and flag unusual activity that might indicate theft or fraud.  
  • By implementing AI for real-time monitoring, utilities can reduce energy losses, ensure accurate billing, and improve overall revenue collection. 
• AI Research and Development Hub Focused on Energy Transition: India should invest in creating AI-focused R&D hubs within its leading energy institutes, such as the National Institute of Solar Energy (NISE) and the Indian Institute of Technology (IITs).  
  • These hubs would focus on developing AI-driven solutions tailored to India’s energy transition, such as energy-efficient technologies, solar optimization, smart grid systems, and emissions reduction techniques.  
  • By fostering innovation in energy AI, India can lead the way in creating low-carbon energy systems. 
• Implement AI-Enabled Regulatory Oversight and Grid Cybersecurity: As AI systems manage increasingly complex energy operations, the need for robust cybersecurity is paramount.  
  • India should implement AI-driven regulatory frameworks that enable continuous monitoring of AI systems’ performance, security vulnerabilities, and compliance with energy regulations.  
  • Furthermore, AI should be used to protect energy infrastructure from cyberattacks by detecting threats in real-time and responding autonomously to safeguard critical assets. 

Conclusion:  

AI has the potential to revolutionize India’s energy sector, driving the country toward its ambitious sustainability goals. By investing in AI-driven solutions for grid management, renewable energy integration, and decentralized energy systems, India can create a more efficient, resilient, and sustainable energy future. This approach will directly contribute to achieving SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action) by enhancing energy access, fostering innovation, and mitigating climate change. 

Drishti Mains Question:

Discuss the role of Artificial Intelligence (AI) in India's energy transition, focusing on its potential to optimize renewable energy integration, enhance grid management, and reduce transmission losses. What measures can be taken to ensure a successful AI-driven energy transformation?

UPSC Civil Services Examination, Previous Year Questions (PYQ)

Prelims 

Q. According to India’s National Policy on Biofuels, which of the following can be used as raw materials for the production of biofuels? (2020)

1. Cassava 
2. Damaged wheat grains 
3. Groundnut seeds 
4. Horse gram 
5. Rotten potatoes 
6. Sugar beet 

Select the correct answer using the code given below:  

(a) 1, 2, 5 and 6 only  

(b) 1, 3, 4 and 6 only  

(c) 2, 3, 4 and 5 only  

(d) 1, 2, 3, 4, 5 and 6  

Ans: (a)

Q. In India, the steel production industry requires the import of (2015)

(a) saltpetre   

(b) rock phosphate  

(c) coking coal   

(d) All of the above  

Ans: (c) 

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Mains  

Q. Describe the benefits of deriving electric energy from sunlight in contrast to conventional energy generation. What are the initiatives offered by our government for this purpose? (2020)

Q. “In spite of adverse environmental impact, coal mining is still inevitable for development”. Discuss. (2017).