Written by Mbuvir Burida, Yuseu young energy ambassador
Have you ever asked ChatGPT what the world's most pressing challenges are? It ranks climate change as number one. So why not use the technology behind ChatGPT to solve that challenge?
The most important way to mitigate climate change is the transition from fossil fuels to renewable energy, or the energy transition. This involves increasing the integration of variable renewable energy sources into the power grid. Therefore, more powerful and innovative tools will be needed to plan and operate the power grid to ensure a safe and reliable power grid as the energy transition progresses.
This need comes at a time when artificial intelligence (AI) is making breakthroughs, mimicking some aspects of human intelligence through large-scale data analysis and related domain knowledge to produce results. Digitalization of the grid (e.g. smart meter, sensors, digital twins, etc.) provide large amounts of data, making AI uniquely positioned to support the energy transition. But can AI solve all power grid challenges?
More reliable power grid forecasting
The predictive capabilities of AI models are a game-changer for the energy sector, from energy generation to consumption to energy markets. One of the main applications is the prediction and optimization of solar and wind energy generation. For example, AI models use weather data in conjunction with historical measurements to predict energy production and consumption for grid planning.
For example, Elia, the Belgian electricity grid operator, AI-based tool reduces system imbalance prediction error by 41% It's part of an effort to keep grid frequencies stable as renewable energy integration increases. This predictive ability of AI models is also used for predictive maintenance of wind farms and power plants. power line. Therefore, AI-based algorithms facilitate real-time monitoring and control of power transmission and distribution, enabling dynamic adjustments in response to fluctuating energy supply and demand.
Additionally, AI algorithms can automatically detect faults, generate real-time power restoration strategies, and switch to backup power sources, reducing system downtime and improving power system reliability. Therefore, AI not only facilitates grid management and renewable energy integration, but also promotes a more efficient, reliable, and secure power grid.
On the energy consumption front, AI-powered energy management systems have made significant advances. These energy management systems optimize energy usage in the following ways: Learn user preferences and adapt to other external events such as weather conditions and electricity pricesFor example, Belgian technology startup Pleevi has developed a Machine learning-based algorithm to control electric vehicle chargingreduce electricity costs by up to 30% while increasing the utilization of projected local energy generation. Meanwhile, ABB, a Swedish-Swiss electrification and automation company, AI-based tools to predict and manage energy consumption peaks Installed in commercial and industrial buildings to allow large consumers to avoid peak demand charges.
Advanced technology comes with risks and obstacles
While significant progress has been made, integrating AI in the energy sector remains a challenge due to the complexity of regulatory frameworks, ethical considerations, and the multifaceted nature of energy systems. Security concerns and data privacy issues raise important questions regarding the safe use of AI in the energy sector and thus regulatory compliance. European Artificial Intelligence Law. Furthermore, the environmental impact of manufacturing AI hardware and the high consumption of energy and water in data centers highlight several obstacles that must be addressed for sustainable use of AI. Moreover, the decision-making process of AI algorithms often remains inexplicable and unexplainable. All these aspects make the adoption of AI-based solutions difficult for users due to significant energy security and financial implications.
Will AI solve all the grid challenges associated with the energy transition?
As synergies between AI and the energy sector continue to expand, interdisciplinary collaboration and a commitment to ethical and responsible AI deployment remain essential to realizing the full potential of this intersection. However, the promise of fully autonomous systems where AI coordinates every aspect of the grid is still far from reality, given the hurdles mentioned above. In reality, integration is a continuous process, characterized by gradual achievements and new challenges.
In 2026, the European Commission will adopt a strategic roadmap for digitalization and AI in the energy sector, aiming to harness the potential of digital and AI technologies while mitigating the associated risks.
This opinion piece was produced in cooperation with European Sustainable Energy Week 2026. reference ec.europa.eu/eusew For open calls.
