Artificial intelligence (AI) in the energy sector of the future

Learn more about the potential of Automated Machine Learning and Artificial Intelligence (AI) in the energy industry.

Climate change and global net-zero targets pose significant challenges for the energy sector. Beyond expanding renewable sources such as wind and solar, the focus is increasingly on their integration into the energy system. Since wind and solar capacities are inherently variable, intelligent solutions like energy storage, smart grids, and flexible load management are needed to ensure supply security and progressively replace fossil fuel reserves.

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Balance between offer and demand

Paradoxically, energy inefficiencies can occur even on days with abundant wind and sunshine. If renewable sources generate more electricity than expected, the grid can become overloaded without intelligent control, leading to unnecessary energy consumption and additional CO₂ emissions. A key challenge is therefore to optimally balance supply and demand. Machine learning plays a crucial role here — both for accurate renewable energy forecasting and for the real-time optimization of smart grids to maximize efficiency and sustainability.

Reliable prognoses for renewable energy sources

Forecasting electricity generation from wind and solar installations has already made significant progress. In Australia, energy production could be predicted over a full year with approximately 80% accuracy. In the U.S., IBM, in collaboration with the government, developed technologies to forecast wind and solar conditions for the next 15–30 days. Similarly, the British AI company DeepMind uses weather and turbine data to reliably predict wind turbine output up to 36 hours in advance. Such precise forecasts provide the foundation for efficient smart grids and optimized energy integration.

Smart Grids

Complementing accurate energy forecasts, smart grids help balance supply and demand more effectively. Smart meters collect consumption data directly from end-users and communicate it in real time through the network infrastructure. This enables efficient management of energy flows and reduces peak loads. At the same time, dynamic pricing allows consumers to shift energy-intensive activities to cheaper off-peak periods, further relieving the overall system.

Machine learning as part of the solution

There is enormous potential in general for machine learning and artificial intelligence to manage the problem of the balance between offer and demand in energy matters. Besides the two major areas of reliable forecasts and smart grids, many other aspects come into play for which great advances are to be expected, such as prevention of power theft or detection and prediction of power failures.

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