A new framework, Engineering-Grounded AI (EGAI), has been developed to integrate artificial intelligence into power and energy systems education. This framework, presented as a collection of open, executable Jupyter notebooks, aims to lower the entry barrier for power engineers and interdisciplinary learners. It progresses from foundational deep neural networks to more advanced applications like deep reinforcement learning for battery control and physics-informed neural networks for system dynamics. The initiative is supported by an IEEE online course and webinar series, which has seen significant engagement, indicating a strong demand for hands-on AI training in the power sector. AI
IMPACT Accelerates the integration of AI into power engineering education, addressing a critical need for skilled professionals in grid modernization.
RANK_REASON The cluster describes a new framework and course for applying AI in power systems education, based on a research paper and IEEE publication.
- 5-bus system
- artificial intelligence
- convolutional neural network
- deep neural network
- deep reinforcement learning
- Engineering-Grounded AI
- Google Colab
- Hugging Face
- IEEE Power & Energy Society
- Institute of Electrical and Electronics Engineers
- large-language models
- physics-informed neural networks
- power engineering
- Project Jupyter
- CNBC
- Gridex AI
- Texas
- United States Department of Energy
- U.S. electrical grid
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