After pre-training on a large text corpora, the model can be fine-tuned for specific use cases in the energy sector. Additionally, Reinforcement Learning from Human Feedback enhances the model through human input. By connecting to external knowledge sources, GenAI can further enhance its capabilities. From image synthesis to data compression to modeling complex processes, the possibilities are endless.

Fraunhofer and TenneT now identified potential use cases for GenAI in the energy sector based on various criteria such as expected value, effort, and resource availability. The mentioned use cases could be:
1. Optimizing workflow processes for TenneT's PUC department employees.
2. Facilitating access to job-relevant information for new hires.
3. Simplifying the presentation of the impacts on business models for the general public during the energy transition.
4. Streamlining maintenance processes for power grids to be more cost-effective and efficient.

Are you ready to shape the future of the energy industry with us? Learn more about how the various use cases are classified on the rating scale!