Mbuwir Brida, EUSEW Young Energy Ambassador, explores how artificial intelligence can help solve power grid challenges and support the energy transition.
Ask ChatGPT about the world’s biggest challenge, and it often points to climate change. That raises an obvious question. Can the same technology behind AI tools help address it?
The most effective way to tackle climate change is to reduce reliance on fossil fuels and expand renewable energy. This shift, known as the energy transition, depends on integrating variable renewable sources into the power grid. As renewables grow, grid planning and operation become more complex. Therefore, new and smarter tools are essential to keep the system stable and secure.
At the same time, artificial intelligence is advancing at a rapid pace. AI systems analyse large volumes of data and apply domain knowledge to generate insights and predictions. Meanwhile, power grids are becoming more digital through smart meters, sensors, and digital twins. As a result, the energy sector now produces vast datasets. This combination places AI in a strong position to support the energy transition. Still, it raises a critical question. Can AI solve all grid challenges?
Better forecasting for a more reliable grid
AI’s predictive capabilities are already changing the energy sector. They affect generation, consumption, and market operations.
One key application involves forecasting solar and wind power output. AI models combine weather data with historical measurements to predict generation and demand. Grid operators then use these forecasts to plan system operations.
For example, Belgium’s transmission system operator, Elia, developed an AI-based tool that reduced system imbalance forecast errors by 41 percent. This improvement helps maintain grid frequency as renewable penetration increases.
In addition, AI supports predictive maintenance. Operators now detect faults in wind turbines and power lines before failures occur. As a result, they can act earlier and reduce outages.
AI also enables real-time grid monitoring and control. Algorithms adjust operations as supply and demand change. Moreover, automated fault detection and restoration systems reduce downtime by switching to backup sources faster. Together, these tools improve grid reliability, efficiency, and security.
Smarter energy use on the demand side
AI is also reshaping how energy consumers manage electricity use.
AI-driven energy management systems learn user behavior and respond to weather conditions and price signals. This allows them to optimise consumption automatically.
For instance, Belgian startup Pleevi uses machine learning to manage electric vehicle charging. Its system cuts electricity costs by up to 30 percent while encouraging the use of locally generated renewable power.
Similarly, ABB has developed AI tools to predict and manage demand peaks in large buildings. These solutions help commercial and industrial users avoid high peak demand charges. Consequently, they reduce costs while easing pressure on the grid.
Risks and roadblocks remain.
Despite progress, several challenges slow wider adoption.
Regulatory complexity remains a major issue. Ethical concerns and data privacy risks also require careful handling. In Europe, AI solutions must comply with the Artificial Intelligence Act, which adds another layer of scrutiny.
There are environmental concerns as well. Manufacturing AI hardware consumes resources, while data centres require large amounts of energy and water. In addition, many AI models operate as black boxes. Their decision-making processes often lack transparency, which creates accountability concerns.
Because power systems involve high security and financial risks, these issues make users cautious about adopting AI-based solutions.
Can AI solve every grid challenge?
The partnership between AI and energy will continue to grow. However, progress depends on collaboration across disciplines and a strong focus on responsible AI use.
Fully autonomous grids, where AI controls every function, remain far from reality. Technical, regulatory, and ethical hurdles still stand in the way. Instead, integration will likely advance step by step, with steady gains and new challenges along the way.
Looking ahead, the European Commission plans to adopt a Strategic Roadmap for digitalisation and AI in the energy sector in 2026. The goal is to unlock the benefits of these technologies while managing their risks.
This opinion editorial is produced in cooperation with the European Sustainable Energy Week 2026.
