Artificial intelligence and renewable energy: how can AI help accelerate the energy transition?

How is artificial intelligence used in renewable energy?

When we talk about artificial intelligence applied to the energy sector, we are not referring only to generative AI tools. The concept encompasses different technologies capable of processing data, learning from it, recognising patterns and making predictions.

A solar PV plant, wind farm, energy storage system or electricity grid continuously generates information. Weather data, production levels, temperatures, vibrations, equipment condition, electricity demand and prices are just some examples.

Analysing all this information makes it possible to better understand how assets are performing and, above all, to anticipate what may happen next.

Predicting how much energy the sun and wind will produce

One of the most relevant applications of AI in renewable energy is electricity generation forecasting.

Models can combine historical production data with weather forecasts, real-time information and other variables to estimate more accurately how much electricity a solar or wind installation will generate over the following hours or days.

Solar irradiation, cloud cover, temperature, wind speed and the characteristics of each installation can all influence production. Improving these forecasts makes it possible to anticipate changes in generation and facilitates the integration of greater amounts of renewable energy into the electricity system.

Anticipating potential faults

Another area with significant potential is predictive maintenance.

Traditionally, maintenance at an installation can be carried out preventively, following a defined schedule, or correctively once a fault has already occurred.

Combining sensors, monitoring and artificial intelligence models creates a third possibility: continuously analysing equipment performance to identify anomalies that could indicate an emerging problem.

For example, unusual changes in temperature, vibration or the performance of specific components may indicate that something is beginning to operate differently from what would normally be expected.

The aim is not to replace people's technical expertise, but to provide more information to support better decisions about when and where to act.

Smarter electricity grids

The energy transition is also making the electricity system more complex. Alongside large-scale power plants, there are increasingly more solar and wind installations, energy storage systems, self-consumption, electric vehicles and other distributed resources.

Managing all these elements requires continuous visibility of what is happening across the grid and an understanding of how generation and demand may evolve.

Artificial intelligence can help analyse this information, predict potential congestion, detect anomalies and optimise electricity flows.

Optimising energy storage

Energy storage is another area where artificial intelligence can play a relevant role.

Batteries make it possible to store electricity when it is available and use it later. But deciding when to charge, when to keep energy stored and when to discharge depends on numerous variables.

Algorithms can combine forecasts of renewable generation and demand, weather conditions, battery state of charge and grid conditions to help optimise these decisions.

This capability is also connected to one of the major challenges of electricity systems with high levels of renewable penetration: curtailment, which occurs when some of the electricity that could be generated has to be limited because the system cannot integrate it at that particular moment.

Better generation forecasting, combined with storage and more intelligent management of the grid and demand, can help reduce these situations.

AI also needs energy

The relationship between artificial intelligence and energy has two sides.

While AI can help optimise certain energy processes, artificial intelligence models require significant computing capacity. This is concentrated mainly in data centres, whose electricity consumption is increasing rapidly.

According to the IEA, global electricity consumption by data centres grew by 17% in 2025, while consumption specifically associated with AI-focused data centres increased by 50% in the same year.

The agency estimates that global electricity consumption by data centres could reach approximately 945 TWh by 2030.

What will AI's role be in the future of renewable energy?

Artificial intelligence does not eliminate the variability of solar and wind power or solve the challenges of the energy transition on its own. Nor does it replace infrastructure, storage, electricity grids or the expertise of the professionals who design and operate the system.

Its main value lies elsewhere: turning large amounts of data into useful information to anticipate, optimise and support better decisions.

More accurately predicting renewable generation, identifying potential problems before they cause a fault, optimising the use of energy storage or managing increasingly complex electricity grids are some examples of how artificial intelligence can contribute.

In an increasingly electrified, renewable and digitalised energy system, the combination of technology, data and human expertise will be one of the keys to making more efficient use of available energy resources and advancing the energy transition.