Roberto Navarro Aragay
Artificial Intelligence has become part of nearly every field, supporting and sometimes replacing human intellectual activity. The energy sector is no exception. In fact, it may be one of the fields where AI has the most to offer.
The four recognised megatrends shaping the energy world, known as the 4Ds (Decarbonisation, Decentralisation, Digitalisation, and Democratisation), already signal how central AI is becoming to the efficiency of current and future energy systems. But making that happen is not straightforward. It requires people who understand both energy and AI, and that combination of skills is still rare. Closing that gap is precisely what AI4GreenDeal, an EU-funded initiative coordinated by InnoEnergy, was created to do.
Modern energy management, with multiple supply points, intermittent renewable generation, and an increasingly active consumer, demands rapid and adaptable decisions that are not always possible without digital technologies. Here is where AI is proving its value.
Production
Estimating how much energy a solar farm will generate tomorrow, or how strongly the wind will blow across a turbine array, used to involve a significant amount of guesswork. AI has changed that. Machine learning models now process weather data alongside historical generation records to produce precise, hour-by-hour forecasts with a level of granularity that conventional methods struggle to match.
Maintenance is another area where AI delivers real results. Sensors fitted to wind turbines and solar installations can detect early signs of wear before they affect output. When combined with drone inspection data, these systems allow operators to intervene at exactly the right moment, not prematurely and not only after a costly breakdown. Building reliable training datasets for these models remains a genuine challenge, but the results seen in real projects are increasingly difficult to dismiss.
Transport and Distribution
The electricity grid of a generation ago was comparatively straightforward: power flowed in one direction, from large plants to consumers. That description no longer fits most European grids. Energy now flows from rooftop solar panels back into the network, from neighbourhood storage systems to homes, and from wind farms whose output can shift dramatically within the hour.
Keeping voltage and frequency stable while deciding in real time when to store energy and when to release it requires data-processing capabilities that only machine learning can provide at scale. Algorithms help operators make sense of constantly changing conditions and coordinate thousands of small decisions that, together, keep the lights on.
The questions AI raises here are not purely technical. Deploying algorithms across critical infrastructure brings accountability and governance issues to the foreground. The Master’s in Advanced Energy Systems and AI, developed under AI4GreenDeal, treats these questions as part of the curriculum and not as an afterthought. When an automated system makes a consequential decision about grid stability, transparency about how that decision was reached matters to regulators, network operators, and consumers. These are active debates in the sector, not settled questions.
Consumption
For most of the past century, consuming electricity was a passive act: you turned something on and later paid the bill. That pattern is changing. Across Europe, more households now own solar panels, home batteries, or electric vehicles, and with them comes a new set of daily decisions about when to charge, whether to sell surplus energy back to the grid, and whether tonight’s spot price makes it worthwhile to shift energy use by a few hours.
The cognitive load of handling these choices is real. AI can take most of it off the table by running quietly in the background, reading price signals and weather forecasts, and adjusting consumption or storage accordingly. The consumer benefits from smarter, cheaper, and often cleaner energy use without needing to track every signal personally.
The people the transition needs
Running AI systems across energy production, grid management, and consumption requires people who understand both the technology and the energy systems it operates within: the physics of a grid in transition, the limitations of real-world datasets, and the governance frameworks now being built around algorithmic decision-making. That combination of skills is still rare.
This is not just an industry observation. As we explored in Europe has named AI in energy a strategic priority. Now it needs the engineers to deliver it., the European Commission has now positioned this skills gap as a strategic concern at the highest policy level. Closing that gap is the core aim of AI4GreenDeal, which brings together universities, industry partners, and research institutions to develop precisely these capabilities. The Master’s in Advanced Energy Systems and AI trains engineers and data scientists to work at this intersection, using real datasets and real operational problems rather than purely theoretical scenarios.
The energy transition depends on technology. Just as importantly, it depends on people who know how to deploy that technology responsibly and effectively.
Roberto Navarro Aragay is a Professor and tutor at ESADE Business School on InnoEnergy Masters+ programmes, and CEO of a renewable energy company.
AI4GreenDeal is co-funded by the European Union under the DIGITAL programme, supporting Europe’s strategic priorities in advanced digital education, innovation and the green transition.