Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework
作者: Junjie Yin, Buxin She, Xinyu Feng, Fangxing, Li
分类: eess.SY, cs.AI
发布日期: 2026-08-03
备注: 10 pages, 10 figures, 3 tables
💡 一句话要点
提出可执行框架以降低电力系统AI教育的入门门槛
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 人工智能 电力系统 深度学习 教育框架 模块化学习 工程基础AI 可执行模块
📋 核心要点
- 现有研究多集中于特定应用，缺乏可重用的教育材料，导致新手学习AI面临障碍。
- 本文提出了一种开放的可执行模块库，旨在通过逐步递进的方式降低电力系统中AI的学习门槛。
- 该框架已吸引590多名参与者，显示出其在教育领域的实际影响和需求。
📝 摘要（中文）
人工智能（AI）在电力与能源系统中日益重要，支持建模、预测、优化和控制。然而，现有研究多集中于特定应用，缺乏可供新手或跨学科学习者重用的材料。为此，本文提出了一种工程基础的AI（EGAI）框架，旨在降低电力系统中AI的入门门槛。该框架包含一个开放的可执行模块库，模块按照难度逐步递进，涵盖核心AI概念与电力系统任务的映射。所有模块均以Jupyter笔记本形式发布，支持本地或Google Colab运行，并通过IEEE在线课程和PES网络研讨会进行传播，吸引了590多名现场参与者，显示出强烈的需求和影响力。
🔬 方法详解
问题定义：本文旨在解决电力系统中AI教育的入门障碍，现有方法多为特定应用，缺乏系统性和可重用性，导致新手难以掌握核心概念。
核心思路：提出工程基础的AI（EGAI）框架，通过开放的可执行模块库，按照难度逐步引导学习者掌握AI在电力系统中的应用。
技术框架：框架包含多个模块，分别为基础深度神经网络（DNN）模板、领域耦合卷积神经网络（CNN）和前沿模块（如DNN优化、深度强化学习等），所有模块均以Jupyter笔记本形式发布。
关键创新：最重要的创新在于将AI工作流程与电力系统领域规则相结合，避免了传统方法的黑箱特性，使学习者能够理解和应用AI技术。
关键设计：模块设计遵循逐步递进的原则，涵盖从基础到前沿的多种技术，确保学习者能够在实际任务中应用所学知识。
🖼️ 关键图片
📊 实验亮点
该框架通过逐步递进的模块设计，成功吸引了590多名参与者，成为IEEE PES网络研讨会中最受欢迎的课程之一，显示出其在电力系统AI教育中的实际效果和广泛需求。
🎯 应用场景
该研究的潜在应用领域包括电力系统的建模、优化和控制等，能够为电力行业的工程师和研究人员提供实用的学习工具，促进AI技术在电力系统中的广泛应用，提升行业整体技术水平。
📄 摘要（原文）
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.