The ongoing transformation of the power sector and the emergence of disruptive technologies necessitate the revamping of existing sector systems and processes. Artificial intelligence (AI)-based solutions are increasingly being integrated across the sector to enhance system flexibility, network visibility and efficiency.
Energy demand patterns and infrastructure requirements are evolving rapidly. The integration of new loads such as large data centres, green hydrogen projects and other bulk loads, along with the increasing penetration of electric vehicles (EVs), is creating new power demand patterns and increasing the need for more robust grid infrastructure. At the same time, the rapid growth of inverter-based resources, distributed energy resources (DERs) and storage systems is enabling localised energy generation and consumption. Smart grids are being implemented to improve efficiency, reliability and real-time management of energy flows.
In this emerging scenario, traditional approaches to network management are proving inadequate. Conventional systems are unable to effectively handle increasing system complexity, rising data volumes, renewable energy variability and the integration of smart grids and smart meters.
Against this backdrop, AI adoption in the Indian power sector is gaining momentum. Several projects are already operational, while many others are under development across utilities. AI-based solutions are finding applications across the entire power sector value chain, with deployments across generation, transmission, distribution, corporate services, operations, cybersecurity and environment and safety functions. These initiatives are focused on predictive intelligence, asset maintenance, digital twins, automation, knowledge management and robust cybersecurity.
AI is increasingly being deployed for asset management across the value chain. Predictive AI models are being used to forecast equipment failures in hydro, solar and thermal plants, enabling proactive maintenance. AI-based solutions are also enhancing system reliability and resilience through early detection of abnormal system behaviour, predictive maintenance and early warning systems. These solutions facilitate faster corrective actions, improved grid resilience, rapid contingency evaluation and enhanced situational awareness.
AI for forecasting demand and renewable energy generation
One of the most suitable use cases of AI in the power sector is in demand and renewable energy forecasting. The deployment of AI is improving solar power forecasting for more accurate day-ahead scheduling, helping optimise transmission networks for enhanced reliability and address several challenges associated with system and network variability.
Power system operator, Grid Controller of India Limited (GRID-INDIA), is leading an AI initiative in this domain. In collaboration with the India Meteorological Department, GRID-INDIA has developed the Grid Astra Portal for AI-based demand and renewable energy forecasting. The portal features an AI-driven demand forecasting module for all-India, regional and state-level forecasts using advanced machine learning (ML) models such as Random Forest and XGBoost (extreme gradient boosting). The platform integrates historical demand trends with weather parameters and other time-based variables for improved forecasting accuracy, and supports multiple forecasting horizons (including intra-day, day-ahead, weekly and monthly forecasts) as well as special-day forecasting features. The platform also supports short- and long-term demand forecasting, facilitating the implementation of advanced demand response strategies for load-serving entities and consumers.
With regard to renewable energy forecasting, the platform also offers an AI-based module for all-India interstate transmission system-connected wind, solar and hybrid renewable energy plants. The module combines historical generation data with weather parameters to improve forecasting and scheduling accuracy for renewable energy generation.
Renewable energy developers are also rapidly adopting AI-based forecasting and scheduling solutions. These solutions help developers account for a wide range of variables, including parameters that are localised in nature. Plant-level deployment of AI enables better assessment of site-specific parameters affecting renewable energy generation. The solutions integrate data across multiple data streams, including site-level instrumentation and analytics. These solutions also continuously learn from past deviations and improve prediction accuracy. As the deviation settlement mechanism becomes more stringent, developers are increasingly investing in AI-based forecasting models for renewable energy plants.
Modernising generation fleet
Power gencos are increasingly deploying AI and digital technologies to improve operational efficiency, optimise plant performance and enhance flexibility amid growing renewable energy integration. AI-based solutions are being used for predictive maintenance, emissions monitoring, scheduling and real-time performance optimisation. Utilities are also leveraging AI, internet of things (IoT), supervisory control and data acquisition (SCADA) systems and digital twins to improve asset management and enable more data-driven operations. AI-based solutions are being deployed for critical applications such as boiler tube leakage prediction, ash dyke health monitoring and hydro dam safety, including the integration of multi-agency data for real-time flood alerts and enhanced dam safety in hydropower plants.
Several leading gencos have already launched AI-driven initiatives, with NTPC Limited deploying advanced AI-based monitoring systems across thermal and renewable power plants for predictive analytics, anomaly detection and maintenance optimisation. NHPC Limited is deploying AI-based early warning systems for dam safety and flood forecasting, while also exploring digital twin technologies for operator training and crisis management. JSW Energy is using AI-driven renewable forecasting solutions for improved scheduling and deviation management. Tata Power is leveraging AI platforms for renewable forecasting, energy trading, grid management and rooftop solar quality inspections. Meanwhile, SJVN Limited and ReNew are exploring AI applications for predictive maintenance, project risk assessment and renewable energy forecasting.
AI-enabled T&D network management
Transmission utilities are increasingly deploying AI and digital technologies for predictive maintenance, dynamic line rating, transmission line inspection and real-time grid monitoring. Utilities are leveraging drones, IoT sensors, analytics platforms and AI-enabled image processing tools to improve asset management, operational efficiency and renewable energy integration. AI-enabled drones and satellite imagery are being used to detect transmission line defects and vegetation encroachment, while advanced diagnostic solutions such as sweep frequency response analysis are supporting transformer health assessment. Overall, AI-based systems can detect abnormalities across transmission assets on a real-time basis and undertake corrective actions.
Power Grid Corporation of India has deployed AI- and ML-based drone inspection systems for transmission line patrolling and developed PG-AMRIT, an AI-enabled asset management platform that identifies defects in transmission towers using GPS-tagged photographs. The utility is also leveraging AI-driven analytics for outage management, predictive maintenance and equipment health monitoring through its PALMS platform, along with AI- and satellite-based vegetation management tools to forecast vegetation growth along transmission corridors.
Similarly, IndiGrid is deploying drone- and AI-based systems for defect identification and has partnered with IBM to develop DigiGrid, an AI-enabled asset management platform for predictive maintenance and lifecycle optimisation of transmission assets. The company is also using image analytics for automated defect detection across transmission infrastructure.
In the distribution segment, utilities are deploying AI-based solutions to improve network reliability, asset management and customer experience. AI-enabled image analysis is being used to detect insulator cracks, contamination and flashover risks; pole lean and cross-arm misalignment; low ground and building clearances; and vegetation proximity to live lines. These systems also provide centralised visibility of field findings, enabling faster corrective action. A case in point is Eastern Power Distribution Company of Andhra Pradesh Limited (APEPDCL), which has deployed a mobile-based inspection solution in which field staff capture geo-tagged images that are analysed using AI to identify safety and reliability issues in lines and insulators.
AI is also being deployed for smart meter data analytics and distribution transformer (DT) health monitoring. At the DT level, AI is being used to monitor load survey data from smart meters, enable communication between DT meters and downstream smart meters for improved load visibility, detect low-voltage DTs and chronic problem areas, and provide visibility into phase imbalance and load concentration. AI and digital tools are also being deployed for power quality monitoring using load survey data, supporting early detection of voltage quality issues at the low tension (LT) and 11 kV levels. This, in turn, enables targeted action for chronic low-voltage and over-voltage cases while improving reliability tracking through standard power quality indices. In parallel, AI-enabled consumer analytics, chatbot-based grievance redressal systems and predictive outage management tools are helping discoms improve response times, enhance service delivery and provide a better customer experience.
AI in project execution and management
AI is increasingly being deployed across project planning, engineering, procurement, construction (EPC) and commissioning activities in the power sector to improve execution efficiency and reduce delays. Over the years, utilities and EPC companies have generated large volumes of data through drone surveys, satellite imagery, geographic information system (GIS)-based mapping, SCADA systems, ERP platforms and digital twins. This data is now enabling AI deployment across project management functions.
EPC companies are using AI, analytics, drones, computer vision and digital twins across the project lifecycle to enhance decision-making and project visibility. In the planning and engineering stages, AI is being used for contract analysis, risk assessment, schedule generation, quantity estimation, drawing reviews, bill of materials generation and engineering calculations. AI tools are also supporting procurement through intelligent request for quotation generation, vendor discovery and delivery lead-time prediction.
At the construction stage, AI-enabled drones and computer vision systems are being deployed for site inspections, progress tracking, snag detection and safety monitoring. Predictive analytics tools are helping identify schedule risks and delays at an early stage, while conversational AI assistants are improving access to project and contract information for faster decision-making.
AI for cybersecurity and monitoring
Another prominent application of AI is in 24×7 security operation centres for IT and OT installations. These systems work on the principle of anomaly detection and generate alerts in case of any abnormal system behaviour. One of the key deployments in this area is user and entity behaviour analytics using ML-based baselining. These systems are being deployed for detecting suspicious user accounts attempting to access sensitive data or systems without authorisation, identifying suspicious user-like entities that mimic normal user behaviour, monitoring user activity across the network, detecting suspicious account creation attempts, and speeding up cybersecurity investigations.
AI-based cybersecurity solutions are also being deployed for continuous anomaly detection, early cyber threat identification, faster incident response and intrusion detection. In cybersecurity, AI has a dual role – it acts both as an attacker’s tool for automation and as a defender’s tool for visibility and rapid threat response.
To conclude, while AI-based solutions are being adopted across the power sector, several challenges still remain. Better data sharing and greater transparency across utilities will be important for the wider adoption of AI solutions and for improving their accuracy. In addition, issues such as poor data quality, cybersecurity risks, integration with legacy systems and shortage of skilled manpower need to be addressed.
Priyanka Kwatra
