Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework
Artificial intelligence (AI) is increasingly central to power and energy systems, supporting modeling, forecasting, optimization, and control. Yet most existing works emphasize specialized applications and offer little reusable material for newcomers or interdisciplinary learners, who increasingly rely on large language models rather than building their own. This gap points to a need for engineering-grounded AI (EGAI), in which AI workflows follow established engineering and power-system domain rules rather than acting as task-agnostic black boxes. Motivated by a community survey of researchers and practitioners, which shows 92% report at least one barrier before running an AI model and 9...
| Authors | Junjie Yin, Buxin She, Xinyu Feng et al. |
|---|---|
| Primary category | eess.SY |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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