In October 2025, renewable energy sources surpassed 50% of the nation's electricity generation for the first time , reaching peaks of nearly 52% in November. These figures mark a turning point in Argentina's energy transition, driven by the consolidation of wind and solar farms and by ever-increasing corporate demand for clean energy. Investments of over USD 4.5 billion are projected between 2024 and 2026, particularly in wind projects in Patagonia and solar projects in the Northwest and Cuyo regions.
But this growth has a downside. Investments prioritized expanding generation capacity over developing digital infrastructure: most operators work with basic monitoring and data systems that are not integrated. More wind farms, but less operational intelligence about them . And although renewable energy costs are already lower than fossil fuels—between USD 54 and USD 82 per MWh compared to over USD 100 for gas—the equation is only complete with efficient operation. Added to this are the saturation of transmission networks and the lack of regulatory predictability, which limit the incorporation of new projects. Looking ahead to 2026–2027, growth will depend on expanding transmission capacity, updating the regulatory framework, and accelerating the integration of energy storage systems.
Projecting energy generation: from rough estimates to commercial precision
Renewable energy generation depends on variables that operate simultaneously across multiple timescales (for example, a cold front can reduce solar generation in Northwest Argentina, and a windstorm in Patagonia can take several wind farms offline). None of these situations are unpredictable, as meteorological models have useful forecast horizons of 3 to 7 days. However, their integration into commercial operations requires specialized models that most local operators currently lack.
Generation forecasting algorithms combine historical data on weather, solar radiation, temperature, and local conditions with specific techniques, such as artificial neural networks and machine learning models, to predict production with high accuracy. In a market where energy is traded on the futures or spot market, operating with rough estimates means sacrificing value on each dispatch contract.
Predictive maintenance: anticipating needs before the system requires it
When a wind turbine fails, the cost isn't just the repair: it's the travel time, the mobilization of specialized equipment, and the energy lost during downtime. This combination of costs can far exceed what a properly anticipated preventative intervention would have required. In this context, drone monitoring and thermal imaging are being used in several wind farms across the country to detect anomalies. Their potential is amplified when combined with predictive models that incorporate the evolution of vibrations, internal temperatures, and load patterns. The integration of both layers is a key benefit of data science: moving from a one-off inspection to continuous management of the condition of each piece of equipment.
Real-time operational optimization
Beyond maintenance, machine learning enables real-time operational optimization. Intelligent solar trackers continuously adjust the tilt and orientation of the panels to maximize light capture. Dirt management can be optimized by detecting when contamination significantly impacts performance, thus avoiding unnecessary cleaning routines. And digital twins (virtual replicas of the wind farm that learn from historical data) allow for the simulation of operational scenarios and the evaluation of dispatch decisions before implementation. Global studies show that the combined application of these techniques can increase a wind farm's generation by up to 2%, without expanding installed capacity.
Demand forecasting: a strategic variable
The Argentine electricity sector is moving towards greater flexibility in the relationship between generation and consumption. In this scenario, anticipating how much energy the system will demand—and when—is as strategic as knowing how much will be generated. This problem shares its mathematical structure with winter gas demand forecasting: a climatic variable as the central driver, time series with pronounced seasonal patterns, and extreme events as the most costly scenarios for error. The methodological transfer between energy sectors is direct, although the Argentine renewable energy industry has not yet systematically incorporated it.
From the energy transition to the opportunity to innovate with data
In a still highly regulated market, some of the value that data science could generate—price optimization, dynamic dispatch management, and energy trading—is contingent on the evolution of the regulatory framework. But this evolution is foreseeable: the world's most developed energy markets already operate with greater flexibility, and Argentina is moving in that direction.
The International Renewable Energy Agency (IRENA) points out that in emerging markets, the adoption of digital tools for predictive management of renewable assets is lagging far behind the pace of installed capacity additions. Closing this gap requires analytical capabilities to transform operational data into actionable decisions. And this capability, unlike physical infrastructure, can be developed now, before the market demands it.
Clearly, Argentina's renewable energy sector has an unparalleled opportunity for innovation: its geography, resources, and favorable economic climate. The operational intelligence to fully leverage these advantages is still under development.
FES's next stops
The Future Energy Summit tour will continue with FES Guatemala on May 14 and FES Mexico on May 19, where key players from the public and private sectors will address the challenges and opportunities linked to the expansion of the energy system, new investment schemes and the execution of strategic projects.
To access the full FES 2026 tour schedule, learn more about it and be part of the meetings, the FES digital platform is available, while all events will have a live broadcast through the official Future Energy Summit YouTube channel.
For inquiries and more information, please contact info@futurenergysummit.com
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