Copertina

Scaling Laws of Machine Learning for Optimal Power Flow

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Liu, Xinyi, He, Xuan, Chen, Yize
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Systems and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Pontryagin Optimal Control via Neural Networks
di: Gu, Chengyang, et al.
Pubblicazione: (2022)

BEAVER: Building Environments with Assessable Variation for Evaluating Multi-Objective Reinforcement Learning
di: Liu, Ruohong, et al.
Pubblicazione: (2025)

Learning the Optimal Power Flow: Environment Design Matters
di: Wolgast, Thomas, et al.
Pubblicazione: (2024)

Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
di: Pineda, Salvador, et al.
Pubblicazione: (2024)

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency
di: Zhou, Yuqi, et al.
Pubblicazione: (2024)

Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids
di: Gillioz, Marc, et al.
Pubblicazione: (2026)

PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
di: Klamkin, Michael, et al.
Pubblicazione: (2025)

An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees
di: Zhang, Ling, et al.
Pubblicazione: (2023)

Reduced Optimal Power Flow Using Graph Neural Network
di: Pham, Thuan, et al.
Pubblicazione: (2022)

A Pontryagin Method of Model-based Reinforcement Learning via Hamiltonian Actor-Critic
di: Gu, Chengyang, et al.
Pubblicazione: (2026)

Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions
di: Za'ter, Muhy Eddin, et al.
Pubblicazione: (2025)

Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow
di: Pham, Thuan, et al.
Pubblicazione: (2024)

Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
di: Rosemberg, Andrew, et al.
Pubblicazione: (2025)

A General Approach of Automated Environment Design for Learning the Optimal Power Flow
di: Wolgast, Thomas, et al.
Pubblicazione: (2025)

C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
di: Liu, Ruohong, et al.
Pubblicazione: (2024)

N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network
di: Pham, Thuan, et al.
Pubblicazione: (2024)

gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
di: Puech, Alban, et al.
Pubblicazione: (2025)

Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting
di: Pham, Thuan, et al.
Pubblicazione: (2024)

Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
di: Chen, Kaixuan, et al.
Pubblicazione: (2024)

Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
di: Pareek, Parikshit, et al.
Pubblicazione: (2023)

DiffOPF: Diffusion Solver for Optimal Power Flow
di: Hoseinpour, Milad, et al.
Pubblicazione: (2025)

Residual Power Flow for Neural Solvers
di: Stiasny, Jochen, et al.
Pubblicazione: (2026)

Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid
di: Zhu, Yongli, et al.
Pubblicazione: (2024)

Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets
di: Li, Meiyi, et al.
Pubblicazione: (2023)

SEAL: SEmantic-Augmented Imitation Learning via Language Model
di: Gu, Chengyang, et al.
Pubblicazione: (2024)

Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context
di: Xu, Jianyu, et al.
Pubblicazione: (2024)

Generalizable Machine Learning Models for Predicting Data Center Server Power, Efficiency, and Throughput
di: Lei, Nuoa, et al.
Pubblicazione: (2025)

Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
di: Nauck, Christian, et al.
Pubblicazione: (2024)

BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
di: Okoyomon, Ehimare, et al.
Pubblicazione: (2026)

Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets
di: Liu, Jinyu, et al.
Pubblicazione: (2024)

Quantum-Enhanced Reinforcement Learning for Accelerating Newton-Raphson Convergence with Ising Machines: A Case Study for Power Flow Analysis
di: Kaseb, Zeynab, et al.
Pubblicazione: (2025)

STO-RL: Offline RL under Sparse Rewards via LLM-Guided Subgoal Temporal Order
di: Gu, Chengyang, et al.
Pubblicazione: (2026)

The Wisdom of the Crowd: High-Fidelity Classification of Cyber-Attacks and Faults in Power Systems Using Ensemble and Machine Learning
di: Abukhousa, Emad, et al.
Pubblicazione: (2025)

Accelerating Optimization and Machine Learning through Decentralization
di: Chen, Ziqin, et al.
Pubblicazione: (2026)

Global Performance Guarantees for Neural Network Models of AC Power Flow
di: Chevalier, Samuel, et al.
Pubblicazione: (2022)

Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
di: Glover, Daniel, et al.
Pubblicazione: (2025)

Solving Conic Programs over Sparse Graphs using a Variational Quantum Approach: The Case of the Optimal Power Flow
di: Le, Thinh Viet, et al.
Pubblicazione: (2025)

Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
di: Liu, Zheng
Pubblicazione: (2026)

Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
di: Li, Sirui, et al.
Pubblicazione: (2025)

A SISA-based Machine Unlearning Framework for Power Transformer Inter-Turn Short-Circuit Fault Localization
di: Liu, Nanhong, et al.
Pubblicazione: (2026)