Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework

• URL: http://arxiv.org/abs/2608.02599v1
• Date: Mon, 03 Aug 2026 17:59:09 GMT
• Title: Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework
• Abstract summary: This paper presents a framework consisting of open, executable module library that lowers the entry barrier for AI in power systems.<n>The modules follow a progressive difficulty ladder that maps core AI concepts onto representative power-system tasks.<n>All modules are released as Jupyter notebooks that run locally or on Google Colab.
• Score: 1.315766151337659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey [64.20429885919701]
  We propose an energy accounting framework identifying computational and communication costs across the Perception-Reasoning-Action cycle.<n>We establish a unified taxonomy spanning model simplification, computation control, input and attention optimization, and hardware-aware inference.<n>We identify open challenges of federated green learning, carbon-aware agency, 6th generation mobile communication (6G)-native Agentic AI, and self-sustaining systems.
  arXiv  Detail & Related papers  (2026-04-09T06:13:59Z)
• Report for NSF Workshop on AI for Electronic Design Automation [56.48556959103223]
  This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA)<n>Bringing together experts across machine learning and EDA, the workshop examined how AI-spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.<n>The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems.
  arXiv  Detail & Related papers  (2026-01-20T23:45:40Z)
• Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference [0.0]
  We present the first embedded FPGA accelerator for BCPNN on a Zynq UltraScale+ system using High-Level Synthesis.<n>Our accelerator achieves up to 17.5x latency and 94% energy savings over ARM baselines, without sacrificing accuracy.<n>This work enables practical neuromorphic computing on edge devices, bridging the gap between brain-like learning and real-world deployment.
  arXiv  Detail & Related papers  (2025-06-23T11:35:20Z)
• E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning [9.957458251671486]
  Ensembling models like Convolutional Neural Networks (CNNs) result in high memory and computing overhead, preventing their deployment in embedded systems. We propose E-QUARTIC, a novel Energy Efficient Edge Ensembling framework to build ensembles of CNNs targeting Artificial Intelligence (AI)-based embedded systems.
  arXiv  Detail & Related papers  (2024-09-12T19:30:22Z)
• NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals [58.83169560132308]
  We introduce NNsight and NDIF, technologies that work in tandem to enable scientific study of the representations and computations learned by very large neural networks.
  arXiv  Detail & Related papers  (2024-07-18T17:59:01Z)
• Green Edge AI: A Contemporary Survey [46.11332733210337]
  The transformative power of AI is derived from the utilization of deep neural networks (DNNs) Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs) Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
  arXiv  Detail & Related papers  (2023-12-01T04:04:37Z)
• Draw your Neural Networks [0.0]
  We present Sketch framework, that uses this GUI-based approach to design and modify the neural networks. The system provides popular layers and operations out-of-the-box and could import any supported pre-trained model.
  arXiv  Detail & Related papers  (2020-12-12T09:44:03Z)
• Neural Dynamic Policies for End-to-End Sensorimotor Learning [51.24542903398335]
  The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space. NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks.
  arXiv  Detail & Related papers  (2020-12-04T18:59:32Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.