Open executable module library of six Jupyter notebooks for AI in power systems

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Evidence · 5 claims (5 for) · 2 studies (2 design), q2 · 0 of 2 report an effect size · 5 claims rest on one study

Description

An open library released by Junjie Yin et al. containing six reproducible Jupyter-notebook modules for hands-on AI-in-power-systems learning, runnable locally or through Google Colab.

Components

Claims

• Most surveyed power-and-energy respondents would use a power-specific hands-on AI tutorial, and few rate generic image-based AI examples as very relevant [+W]
• A DQN-trained battery storage policy reduced complete cost by 5.9% relative to no storage on a synthetic 24-h training-day rollout [+W]
• In the CNN 5-bus power-flow surrogate, pairing model capacity with sufficient training length improves voltage accuracy, and line flows are the more demanding test [+W]
• With identical architectures and budgets, a PINN more closely reproduced swing-equation rotor angle and speed trajectories than an ordinary neural network [+W]
• Module 3 code execution success dropped to approximately 60% before a missing-dependency infrastructure fix, recovering to match the 86–87% rates of Modules 1 and 2 [+W]

Related Products and Programmes

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Key Sources

• Junjie Yin, Buxin She, Xinyu Feng, Fangxing (Fran) Li. (2026). Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework. arXiv preprint. https://arxiv.org/abs/2608.02599

Page metadata

Field	Value
type	product
id	open-executable-module-library-of-six-jupyter-notebooks-for-ai-in-power-systems
title	Open executable module library of six Jupyter notebooks for AI in power systems
description	An open library released by Junjie Yin et al.
status	review
product_kind	software
generated	by process:settle-candidates on 2026-10-10
trust	unverified — no one has confirmed this page's content

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