High-Fidelity Machine Learning Approximations of Large-Scale Optimal
Power Flow

High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow

29 June 2020

Ferdinando Fioretto

Terrence W.K. Mak

Pascal Van Hentenryck

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Papers citing "High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow"

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Title
Towards graph neural networks for provably solving convex optimization problems Chendi Qian Christopher Morris 96 1 0 04 Feb 2025
PowerGraph: A power grid benchmark dataset for graph neural networks Anna Varbella Kenza Amara B. Gjorgiev Mennatallah El-Assady G. Sansavini 46 9 0 05 Feb 2024
Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality Ke Chen Shourya Bose Yu Zhang 52 7 0 07 Dec 2022
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments Seonho Park Wenbo Chen Dahyeon Han Mathieu Tanneau Pascal Van Hentenryck 99 28 0 28 Nov 2022
The intersection of machine learning with forecasting and optimisation: theory and applications M. Abolghasemi 67 2 0 24 Nov 2022
Self-Supervised Primal-Dual Learning for Constrained Optimization Seonho Park Pascal Van Hentenryck 81 51 0 18 Aug 2022
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems Tianyu Zhao Xiang Pan Minghua Chen S. Low 90 10 0 15 Dec 2021
OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets Trager Joswig-Jones K. Baker Ahmed S. Zamzam 52 29 0 01 Nov 2021
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization Bingqing Chen Neural Network Kyri Baker J. Zico Kolter Mario Berges 88 61 0 19 May 2021
Learning-Accelerated ADMM for Distributed Optimal Power Flow David J. Biagioni P. Graf Xinming Zhang Ahmed S. Zamzam K. Baker J. King 59 8 0 08 Nov 2019