A few years ago, artificial intelligence (AI) was more of a concept in the renewable energy sector, with limited applications and no real visibility. Today, it has become a game changer in the sector and informs every aspect of it.
AI’s growing role in the sector is very evident. In earlier Renewable Watch conferences, conversations around the use of AI in the clean energy sector were marginal. Developers, operations and maintenance (O&M) players and other stakeholders acknowledged AI’s potential, but viewed its adoption as a distant prospect. That is no longer the case. What was once a theoretical concept is now being leveraged for real-world applications by organisations such as Google, Vestas, Siemens Gamesa, POWERGRID, SolarEdge and DeepMind.
As solar, wind and hydropower take centre stage, and the world embraces sustainable energy at scale, there are concerns about its integration into the grid. The unpredictability and decentralisation of renewables pose various challenges. This is where AI is stepping in – not merely as a tool, but as a transformative mechanism. Through smarter forecasting, real-time grid management and predictive maintenance, AI is transforming energy generation, integration and consumption. It analyses vast datasets, weather forecasts, generation history and usage patterns to optimise performance and ensure a balance between supply and demand. It also helps reduce downtime, cut costs and enhance the efficiency of renewable energy projects.
To highlight and discuss these trends, Renewable Watch recently held a conference on “AI in Renewables”, bringing together leaders and stakeholders from across the energy spectrum. This cover story is based on the discussions and key takeaways from the event…
AI in O&M
AI plays a transformative role in the O&M of renewable energy projects by driving digital integration, improving efficiency and enabling data-driven decision-making. Companies such as Jakson Green are leading full-scale digital transformations using AI to minimise manual intervention in O&M and prevent generation losses. Developers such as AMPIn Energy Transition are using AI for centralised monitoring, forecasting and scheduling (F&S), with pilots under way to improve safety and optimise plant performance. Blupine Energy is leveraging digital tools for plant performance analytics and reporting, while Fortum is advancing from predictive to prescriptive maintenance by integrating AI with condition monitoring systems and inventory systems. The company is also developing AI models using satellite and weather data for better solar F&S. Apraava Energy is building an integrated platform combining wind and solar asset data to support informed decision-making at all levels, with a strong focus on actionable insights for operational efficiency.
AI has enabled role-based inspections, automated reporting, optimised use of original equipment manufacturer (OEM) tools and better resource utilisation, resulting in significant cost savings of up to 50 per cent, as per Bajrang Ahirwar, Head, Projects and Asset Management, Fortum India. It empowers technicians by automating tasks and optimising manpower deployment. Accurate forecasting and live risk management systems also reduce penalties and enhance efficiency.
At the same time, there is a strong emphasis on building robust cybersecurity frameworks to safeguard increasingly digitalised infrastructure. Companies are aligning with ISO 27001 and Central Electricity Authority standards, and investing in OT security through measures such as air-gapping, unidirectional gateways and red-teaming exercises.
AI in project design
AI is beginning to transform how renewable energy projects are designed, helping in timely project execution and improving cost efficiency. It enhances site selection planning through detailed topographical and climatic analysis. AI also supports land optimisation, particularly for solar projects, by balancing land use, cable losses and cost efficiency.
Design processes are being further optimised through AI-generated initial drafts based on historical project data, while real-time drone-based surveys feed terrain information directly into planning systems. In the construction phase, AI enables real-time project monitoring to ensure adherence to budgets and timelines, facilitating smooth transitions to operations teams.
Furthermore, it is transforming procurement and supply chain management through real-time planning that factors in site conditions, workforce availability and weather forecasts.
Several organisations have been putting AI into practice. At Inox Green, AI-driven site selection has significantly improved planning and deployment by replacing error-prone manual surveys with accurate terrain data. In wind projects, drones are used for line surveys, site mapping, blade cleaning and security, while geotagging tools help address remote-site manpower issues by improving attendance tracking and reducing travel needs. At Rays Power Infra, drones enhance solar plant execution, enabling detailed terrain analysis, route planning and construction monitoring. The use of robotics, particularly for panel cleaning, has reduced water consumption and allowed more flexible plant designs.
AI in F&S
AI is playing an increasingly critical role in the F&S of renewable energy. By integrating AI/machine learning-based forecasting tools into renewable energy management centres, Grid Controller of India Limited aims to improve day-ahead and intra-day renewable energy forecasts.
Shilpy Dewan, Head, Markets, Operations and Digital Serentica Renewables, emphasised that AI systems are most effective when paired with real site performance data, and manual interventions by operators are often still necessary to refine forecasts and handle unpredictable changes. From a scheduling perspective, AI enables compliance with regulatory requirements by providing precise, time-blocked projections. This is particularly important as regulators demand accurate forecasts and impose penalties if developers deviate from the forecasts beyond a percentage band.
AI also helps coordinate generation forecasts across multiple project sites, balance demand-supply mismatches and improve overall grid reliability, which is crucial given the volatility of renewable generation. By enhancing coordination with qualified coordinating agencies, AI supports real-time decision-making in project scheduling by identifying potential deviations at an early stage. Organisations are now using layered AI models, working with multiple forecasters to progressively improve forecasting accuracy. However, data sharing remains a barrier, with developers reluctant to provide performance data due to privacy concerns. This has led to a growing call for regulatory clarity to ensure that such data is used strictly for forecasting purposes.
AI in strategic planning
AI is also being explored for strategic planning to ensure informed, data-driven decisions across the entire renewable project lifecycle. AI tools analyse historical data, energy market trends, weather forecasts and policy changes to support decision-making, especially in selecting the most efficient renewable energy mix and identifying suitable projects for bidding. These tools also assist in tariff optimisation by reducing energy variability, improving O&M efficiency and lowering deviation penalties, which directly influence cost structures and project competitiveness. In engineering and procurement, AI supports strategic choices by evaluating vendor reliability, optimising designs based on location-specific data and assessing component durability, particularly for long-term planning in 25-year projects. AI-driven bid management tools take into account various parameters such as soil type, tracker configuration, module degradation and evolving tariff structures to ensure accurate forecasting of costs and generation potential. In complex hybrid or round-the-clock projects, AI use can contribute to planning by balancing supply, cost economics and policy compliance.
AI’s value also extends to asset management and performance optimisation, with the use of tools such as image analytics, optical character recognition and internet of things for real-time monitoring, diagnostics and predictive maintenance. This reduces operational risks and supports consistent energy generation. In the context of open access and commercial and industrial consumers, AI is increasingly used for scenario analysis, helping evaluate sourcing strategies across geographies and regulatory environments. However, its success depends on robust data governance, adequate infrastructure and clearly defined problems.
Challenges
The rapid advancements in AI technology have created significant opportunities for the renewable energy sector, but they have also introduced new challenges. One of the foremost issues is the integration of legacy systems. Modern renewable plants rely on diverse digital platforms such as SCADA, computerised maintenance management systems, production planning and control and condition monitoring systems. However, integrating these systems into a unified AI-enabled platform is difficult due to compatibility issues with older infrastructure. Additionally, organisational resistance to change and traditional mindsets in the industry slow down digital transformation efforts. Another significant hurdle is data quality and availability. Furthermore, security concerns around plant operations and system access must be addressed before AI can be fully trusted and deployed. Another issue is the rise of AI-driven cyberattacks.
Moreover, AI processing is energy-intensive, with estimates suggesting that training a single AI model can consume up to 284,000 kWh of energy. Data centres alone account for 1 per cent of global electricity demand. The energy requirements for AI computing necessitate the establishment of hyperscale data centres, which should ideally be powered by renewables.
A shortage of trained manpower to manage and operate AI tools also presents a barrier. While AI promises to reduce reliance on large operational teams, the transition still requires skilled personnel who can interpret AI outputs and act accordingly. There is also a lack of proper training and upskilling programmes designed for field teams to effectively utilise digital tools. Finally, the industry must bridge the trust gap in technology. Leaders must first believe in IT and AI solutions themselves before advocating their benefits to wider teams or boards.
The way forward
To fully harness digital technologies, organisations must prioritise high-quality data, integrate operations and empower the workforce. Clean data, primarily from external sources such as OEMs, is essential for deep insights. Aligning IT and OT teams, breaking silos and fostering digital literacy across functions will build the trust and collaboration needed for transformation. Furthermore, organisations should adopt integration protocols such as IoT and open platform communications to create unified data environments. With growing manpower constraints, shifting from calendar-based to condition-based maintenance using intelligent systems can improve efficiency and reduce downtime. Successful transformation also hinges on change management. A “sandwich approach” – combining the top-down vision with bottom-up engagement – ensures inclusive adoption and long-term success.
Net, net, while AI holds immense promise, it is not infallible. It requires careful evaluation with a clear understanding of its limitations and complexities. It must be deployed after due thought and consideration, and refined frequently in order to realise its true potential in the renewable energy space. Implemented strategically, AI could accelerate the transition towards a more decentralised, decarbonised, digitalised and democratised energy future.
Sakshi Bansal
