By Karan Sharma
India’s renewable energy sector is undergoing a transformation. At solar parks, wind farms and control rooms, decisions once made after manual inspection are now being executed by lines of code. The grid, long defined by outages, manual controls and conservative reserve margins, is now becoming a data-driven network guided by sensors, real-time data streams and automated control systems.
The reason artificial intelligence (AI) is gaining traction in the renewables sector lies in the way renewable projects operate. Unlike conventional thermal plants, which run at relatively stable output levels, solar and wind projects are highly variable in their output as they rely on constantly changing environmental conditions. AI, supported by internet of things sensors, advanced analytics platforms and digital twins, provides the computational layer required to process these large volumes of operational data. From improving forecasting accuracy and optimising plant performance to enabling intelligent grid management and supporting project planning, AI applications are now emerging across the renewable value chain.
F&S and grid integration
Forecasting and scheduling (F&S) is one of the most critical operational challenges in renewable systems. The variable nature of solar and wind energy and their dependence on weather conditions means that even small forecasting errors can translate into significant deviations in electricity supply. Traditional forecasting systems rely primarily on numerical weather prediction models, which often struggle to capture local weather variations such as cloud formation and terrain-driven wind patterns.
Therefore, the industry is shifting to AI-based forecasting systems, which use machine learning (ML) models that combine historical generation data, satellite imagery, meteorological inputs and real-time sensor information, to refine short-term generation forecasts. Moreover, as grid discipline becomes stricter, scheduling errors can lead to imbalance penalties. Improved forecasting accuracy allows developers to reduce balancing costs, providing clear and direct financial benefits.
As India’s electricity markets evolve towards real-time trading and ancillary service mechanisms, scheduling is becoming increasingly complex. To address this, AI-driven market platforms and real-time despatch engines are being used to combine generation forecasts with electricity price signals and transmission information to optimise despatch decisions.
Asset performance and O&M
Operations and maintenance (O&M) remain a major cost component in renewable projects. Operating in harsh environmental conditions often leads to equipment failures and costly downtime. Hence, predictive maintenance is arguably the most mature AI application in the field. Sensors on inverters, transformers, turbines and gearboxes stream vibration, temperature and electrical signatures into central platforms. In view of this, AI is increasingly being applied to shift maintenance strategies from preventive to predictive and prescriptive models, where interventions are carried out only when required. At Renewable Watch’s conference on “AI in Renewables”, Bajrang Ahirwar, Head of Asset Management, Fortum India, noted that this shift has led to cost savings of up to 50 per cent, and with even conservative estimates, small availability improvements (of around 1.5-3 per cent) translate into revenue margins of 10-15 per cent.
Several renewable companies have already made the shift. Gentari, for example, has been operating an AI-enabled remote monitoring platform “Hawk AI” for the past eight years. The solution continuously tracks plant performance and generates early warning signals for turbine faults. It combines long-term performance trends with drone-based inspections and sensor data to identify structural damage such as blade cracks, corrosion or mechanical wear. Using AI, Jakson Green reduces manual intervention in plant operations, while Blupine Energy employs digital tools for plant performance analytics and reporting. Fortum is developing ML models that combine satellite and weather data to improve solar generation forecasting.
Smart grids and digital twins
A digital twin is a virtual replica of a physical asset or an infrastructure system that continuously receives data from sensors and operational systems. Meanwhile, smart grids integrate sensors, communication networks and data platforms to provide real-time visibility especially across the distributed renewable landscape.
By simulating real-world operational conditions, digital twins allow operators to test strategies and evaluate performance under different scenarios. For example, in wind projects, digital twins can simulate wind turbine performance under varying wind conditions and optimise pitch and yaw settings to maximise generation. In solar plants, digital twins can identify power anomalies, detect underperforming modules and prioritise maintenance actions. They also help optimise solar tracking systems by analysing real-time weather patterns and predicting the most efficient panel orientation. Beyond individual plants, digital twins are also being explored for power system planning and grid management. By integrating digital twins with smart grid infrastructure, utilities can manage peak loads, identify and mitigate faults, and test system resilience.
This application is also witnessing increasing adoption. For example, in March 2025, Tata Power, in collaboration with Salesforce, announced its plans to create a unified digital platform powered by AI, automation and data-driven insights for distributed renewable projects. State utilities are also experimenting with these technologies. Maharashtra State Electricity Distribution Company Limited entered into an agreement with the Global Energy Alliance for People and Planet in October 2025 to deploy AI and ML and battery storage technologies to modernise the distribution network. Power Grid Corporation of India Limited and several state load despatch centres are also testing AI-assisted control room platforms and digital twin models that simulate grid behaviour under different demand and generation scenarios.
Project design and planning
AI’s applications are also expanding into strategic planning across the entire renewable value chain. In an interview with Renewable Watch in October 2025, Anvesha Thakker, Partner and National Lead – Clean Energy, KPMG India, noted that AI introduces an optimisation layer across siting and resource assessment, bidding and construction planning. For example, ML models, using historical generation data, market trends and policy developments can co-optimise bid configuration and assess risks, thereby reducing the scope for human error. By improving forecasting accuracy and reducing operational risks, AI tools can lower deviation penalties and are therefore also being used for tariff optimisation.
Furthermore, AI is reshaping how renewable projects are designed and executed. By analysing topographical data, historical weather patterns and satellite imagery, as well as with the help of drone-based surveys, AI can identify optimal locations for renewable energy projects. Real-time terrain data collected by these drones can be fed directly into AI-driven planning software, helping developers replace manual surveys, and enabling more accurate planning of turbine installation, equipment logistics and plant layout.
AI is also improving project execution. During the construction phase, digital monitoring platforms track project progress in real time, helping developers manage budgets, allocate resources and identify potential delays early. Robotics is also being explored for tasks such as solar panel cleaning, thereby reducing water consumption and improving plant performance. In engineering and procurement, AI can evaluate vendor performance, analyse component durability and optimise plant designs based on location-specific conditions.
Challenges and the future outlook
Data fragmentation and uneven adoption
The pace of AI adoption across the renewable sector remains uneven remain major barriers, but data platforms can accelerate large-scale deployment. Many companies are still experimenting with pilot projects and proof-of-concept deployments rather than implementing AI solutions at scale. There are concerns around data privacy, integration challenges between operational technology (OT) and information technology (IT) systems, and the cost of scaling digital infrastructure. Additionally, renewable assets are often geographically dispersed and operate through multiple digital platforms, resulting in inconsistent data formats and limited interoperability between systems. Therefore, valuable operational data remains underutilised because it is stored across isolated platforms or proprietary systems.
In a guest article for Renewable Watch in November 2025, Shabir Badra, Vice-President, IT and Cybersecurity, Apraava Energy, noted that the key to unlocking AI’s full potential lies in building unified digital architectures. He emphasises the need to create “one operating platform for one business”, where field-level sensors, operational databases and enterprise decision-making systems are connected through a secure and integrated data pipeline. Furthermore, the creation of a national energy data exchange would enable the secure sharing of anonymised operational data among utilities and developers.
Cybersecurity risks
As renewables become increasingly digitalised, cybersecurity risks are emerging as a critical concern, making stronger OT security architecture and common standards necessary. AI-enabled monitoring systems, cloud-based analytics platforms and remote asset management tools all rely on continuous data connectivity, which can expand the potential attack surface for cyberthreats. At the RENEWSEC 2025 conference held in December 2025, experts highlighted that the integration of legacy control systems with modern cloud and edge computing services has significantly increased risk exposure across energy infrastructure. Most lack modern digital protection mechanisms such as next-generation firewalls or OT-specific intrusion detection systems. Strengthening cybersecurity frameworks will require OT-specific security tools and continuous monitoring systems designed specifically for critical energy infrastructure. Establishing common cybersecurity standards as well as a shift to zero-trust practices could also help in building system resilience.
Skill shortages and limited accessibility remain
There is a shortage of skilled personnel capable of managing and interpreting AI-driven tools. Although AI promises to automate several operational tasks in the long term, the transition phase requires engineers and technicians who understand both renewable energy systems and advanced data analytics. Such interdisciplinary expertise remains limited, especially in the renewable sector. Limited access to specialised AI tools can also discourage smaller developers from adopting advanced digital technologies.
Addressing these challenges will require coordinated policy support. Governments, by establishing common digital infrastructure standards, can encourage developers to adopt interoperable protocols for data capture, communication and cybersecurity. Financial incentives such as tax benefits, innovation grants and pilot funding could also support companies experimenting with AI applications. Furthermore, dedicated training programmes that combine renewable energy engineering with AI and data analytics will be essential to build the specialised talent needed for long-term digital transformation.
AI to lead the way for renewables
In the long term, AI is likely to become a core component of the renewable energy sector, which has already demonstrated its ability to scale rapidly. At its core, the sector is inherently data-rich and algorithms will increasingly define how it operates provided that it is supported by robust digital infrastructure and effective policy frameworks. However, the real test will be ensuring that the intelligence running these systems remains secure, accessible and guided by human decision-making.