Amid rapidly rising electricity demand and constrained generation capacity, State power utilities are increasingly turning to Artificial Intelligence (AI) and Machine Learning (ML) – based technologies to modernise load forecasting and strengthen grid management systems. Officials said these advanced tools are capable of analysing multiple variables, including weather patterns, historical load curves, consumer behaviour and industrial activity, to generate accurate short, medium and long-term demand forecasts. This shift is expected to enable more scientific planning, improve operational efficiency and minimise uncertainties in power distribution. It may be noted that the State’s peak electricity demand has already reached between 19,000 and 20,000 MW and, according to estimates by the Central Electricity Authority, is projected to rise to nearly 22,000 MW by 2027–28. A key factor behind this steady surge is the rapid growth in the number of consumers. Over the past few years, domestic consumers have increased from 12 million to over 12.6 million, while agricultural users have grown from 3.2 million to more than 3.5 million, significantly driving up overall electricity consumption.

 

On the supply side, however, power generation has not kept pace with rising demand. In 2024–25, against a target of 32,100 million units, actual generation was limited to 28,790 million units. Thermal power plants have been underperforming due to a combination of technical constraints, equipment availability issues, coal supply challenges and maintenance-related shutdowns. As a result, during peak demand periods, utilities are often compelled to procure expensive electricity from the open market, increasing the overall cost burden. In this scenario, AI-based demand forecasting systems are expected to play a crucial role in enhancing advance planning capabilities. Load dispatch centres can leverage real-time data to better balance supply and demand, while key operational decisions such as peak load management, unit commitment and economic dispatch can be carried out with greater precision and efficiency.

 

The transmission sector is also witnessing a shift towards AI-driven solutions. Utilities are exploring the use of drone-based inspections of transmission lines, combined with image processing and AI algorithms, for early fault detection, hotspot identification and predictive maintenance. This approach is expected to reduce tripping incidents and improve overall grid reliability. During the summer months, when electricity consumption spikes sharply, the pressure on supply often leads to load shedding in certain areas. The State’s peak demand has risen significantly, from 14,089 MW in 2018–19 to 18,913 MW in 2024, and is now approaching 20,000 MW in 2026. A senior officer of the Electricity Department said that in order to address these challenges, power companies are also working towards diversifying the energy mix by sourcing power from supercritical thermal projects and integrating renewable energy into the grid more effectively. He believe that the adoption of AI and ML-based smart energy management systems will not only enhance the reliability of power supply but also help optimise costs, reduce transmission losses and ensure uninterrupted electricity for consumers.