发表机构
The University of Tennessee; Kansas State University（田纳西大学; 堪萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出可执行实践框架，通过渐进式模块降低电力系统AI入门门槛，经IEEE网络研讨会验证，可满足跨学科学习者对电力专用AI实践课程的需求。
AI 中文摘要
人工智能（AI）在电力与能源系统中的作用日益核心，可支持建模、预测、优化与控制任务。然而现有多数研究侧重专业应用，几乎未为新手或跨学科学习者提供可复用材料——这类学习者越来越依赖大语言模型，而非自行构建模型。这一缺口凸显了对工程导向人工智能（EGAI）的需求，其AI工作流程需遵循成熟的工程与电力系统领域规则，而非作为与任务无关的黑箱。基于对研究人员与从业者的社区调查（显示92%的人运行AI模型前至少遇到一项障碍，94%的人希望开设电力专用实践课程），本文提出一个框架，包含开放的可执行模块库，可降低电力系统AI的入门门槛。模块遵循渐进式难度阶梯，将核心AI概念映射到代表性电力系统任务：（i）用于函数逼近与负荷曲线拟合的基础深度神经网络（DNN）模板；（ii）适用于5节点系统的领域耦合卷积神经网络（CNN）潮流替代模型；（iii）DNN辅助优化、用于电池储能控制的深度强化学习（DRL）、以及用于摇摆方程的物理信息神经网络（PINN）等前沿模块。所有模块以Jupyter笔记本形式发布，可在本地或Google Colab上运行，并通过IEEE在线课程与IEEE电力与能源协会（PES）网络研讨会系列提供。该网络研讨会吸引了590余名现场参会者，是IEEE PES参会人数前十的网络研讨会之一，两周内仓库访问量超344次，验证了调查得出的动机。
英文摘要
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 94% want a power-specific hands-on course. This paper presents a framework consisting of open, executable module library that lowers the entry barrier for AI in power systems. The modules follow a progressive difficulty ladder that maps core AI concepts onto representative power-system tasks: (i) foundational deep neural network (DNN) templates for function approximation and load-curve fitting; (ii) a domain-coupled convolutional neural network (CNN) power-flow surrogate for a 5-bus system; and (iii) frontier modules on DNN-assisted optimization, deep reinforcement learning (DRL) for battery storage control, and physics-informed neural networks (PINNs) for the swing equation. All modules are released as Jupyter notebooks that run locally or on Google Colab and are delivered through an IEEE online course and IEEE Power & Energy Society (PES) webinar series. The webinar drew more than 590 live attendees, which is among the ten most-attended IEEE PES webinars, and over 344 repository visits within two weeks, reinforcing the survey-based motivation.
Comments10 pages, 10 figures, 3 tables
