Keeping pace with a growing economy, and rapidly expanding urban landscape, India’s energy demand is on the rise, with consumption expected to double by 2030, according to the International Energy Agency (IEA). To meet this demand sustainably, the Government of India aims for 500 GW of non-fossil fuel capacity by 2030 and a net-zero target by 2070. These targets align with India’s commitment under the Paris Agreement in reducing dependency on fossil fuels, increasing energy security, and addressing air quality. Artificial Intelligence (AI) emerges as a critical tool for optimising energy management, from predictive maintenance to grid balancing. While AI’s potential in the energy sector is immense, barriers to effective implementation and deployment remain equally significant. Challenges such as data quality and limited AI infrastructure must be addressed to realise the full potential of AI in supporting India’s energy goals and effectively integrating renewable sources into the grid.
The challenges in India
India’s power sector faces significant challenges with respect to reliability, grid stability and energy losses. Despite renewable capacity additions, transmission and distribution (T&D) losses reach 20%-30% of the total electricity generated. With approximately 75% of electricity sourced from coal, the power sector contributes to a staggeringly high percentage of greenhouse gas emissions. To support both decarbonisation processes and rising demand, India’s power sector is increasingly looking toward smart solutions to improve efficiency, reduce emissions, and meet its renewable energy goals
AI is gaining traction as an essential enabler in India’s journey toward a more resilient, low-carbon energy system, particularly in renewable energy forecasting. Machine learning models, for instance, predict renewable generation and demand fluctuations, enabling grid operators to balance energy supply and prevent shortages. AI-driven smart grids also optimise energy flow by detecting faults and reducing transmission losses — a crucial capability for integrating a higher share of renewables.
In consumer-facing applications, AI’s role in energy management extends to real-time monitoring of consumption, and adjusting demand response. AI-powered systems adjust energy supply based on real-time usage, significantly reducing wastage and costs. This is a crucial capability for India, where demand fluctuations are high and peak loads can strain the grid. By analysing consumption patterns, AI also supports consumer-side energy efficiency by encouraging behaviours that reduce peak-hour strain and promote off-peak usage.
AI adoption, realities on the ground
The urban power sector, particularly in tier 2 and tier 3 cities, faces a unique set of realities that need to be addressed for AI to make a meaningful impact.
Despite the promise, practical challenges remain, especially in tier 2 and tier 3 cities, limiting their effectiveness especially in regions with outdated infrastructure, high electricity theft, and frequent outages. These issues along with fragmented data systems hinder the use of AI in energy forecasting and grid optimisation. Financial barriers further complicate the adoption of AI, particularly in smaller utilities as they struggle to meet the high upfront costs and limited government support. In addition, a lack of supportive policy frameworks and guidelines dampens the investments in AI technologies. Additionally, a shortage of AI and data analytics experts limits the sector’s ability to leverage AI solutions, while growing cybersecurity risks highlight the need for robust protections.
Programmes overseas
A successful path forward for India’s energy sector hinges on several critical areas, including technological advancements, human expertise, and comprehensive policy frameworks. Implementing smart grids and smart meters, along with AI-driven software and robust cloud platforms for data storage, are essential for enhancing energy management and real-time data collection. For instance, Barcelona’s smart meter implementation has revolutionised energy management by providing real-time insights, while Los Angeles leverages cloud-based analytics for predictive energy distribution.
Additionally, the sector requires a skilled workforce; initiatives such as Germany’s specialised training programmes are vital for developing machine-learning expertise among energy professionals. Further, job impact assessments must accompany AI advancements, as seen in the United Kingdom’s retraining programmes for workers affected by automation. Empowering consumers, including prosumers, is crucial for effective demand-side management, as demonstrated by Copenhagen’s energy-saving initiatives.
Support for research and development in AI applications, exemplified by Singapore’s investments, will drive innovation in the energy sector. Lastly, robust cybersecurity protocols, such as those implemented in New York, are necessary to safeguard sensitive data against potential threats. Multilateral cooperation and partnerships with Information and Communication Technology (ICT) firms like that of the Tokyo Electric Power Company and its collaboration with grid operators can significantly enhance grid efficiency and reliability through innovative AI solutions.
Social dimension of AI’s role in transition
AI offers transformative potential for India’s energy sector by boosting efficiency, reducing emissions, and integrating renewables, but the challenges remain. Outdated infrastructure, financial and policy barriers, skill shortages, and equity concerns need adequate attention. A collaborative effort involving government support, private investment, and community engagement will be key to ensuring that AI’s benefits are sustainable and accessible across urban and rural areas.
Transition towards integrating AI also risks widening inequalities, especially in underserved regions. For AI to be effective beyond urban centres, it must tackle unique rural challenges, such as unreliable energy supplies and insufficient technological support. Addressing ethical concerns such as fairness and job impacts along with community engagement and workforce reskilling, are essential for a just energy transition across India.
Godwin Paul Adams was an Energy Fellow at Transitions Research. The perspectives presented here draw on work undertaken while Godwin Paul Adams was with Transitions Research. Angelina Chamuah is the Programme Director of Future Foresight Forum at Transitions Research
Published – July 12, 2025 12:34 pm IST