Introduces PowerPhase benchmark for massive-variate power-system forecasting and PowerForge model that achieves best average rank on safety-fidelity metrics across all tested grids.
OPFData: Large-scale datasets for machine learning-accelerated ac optimal power flow
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
fields
cs.LG 5years
2026 5verdicts
UNVERDICTED 5representative citing papers
LUMINA-Bench is a standardized evaluation framework for ACOPF surrogate models that tests generalization across multiple grid topologies using accuracy and physics-constraint metrics.
Develops a heterogeneous GNN workflow on HydraGNN for large-scale OPF surrogate modeling across varied grid topologies and shows that pretraining improves fine-tuning on feasibility and N-1 contingency tasks.
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.
LUMINA derives three design principles for physics-informed foundation models that balance accuracy, constraint satisfaction, and reliability on topology-transferable ACOPF problems.
citing papers explorer
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Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarios
Introduces PowerPhase benchmark for massive-variate power-system forecasting and PowerForge model that achieves best average rank on safety-fidelity metrics across all tested grids.
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LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
LUMINA-Bench is a standardized evaluation framework for ACOPF surrogate models that tests generalization across multiple grid topologies using accuracy and physics-constraint metrics.
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Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
Develops a heterogeneous GNN workflow on HydraGNN for large-scale OPF surrogate modeling across varied grid topologies and shows that pretraining improves fine-tuning on feasibility and N-1 contingency tasks.
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Towards Systematic Generalization for Power Grid Optimization Problems
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.
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LUMINA: Foundation Models for Topology Transferable ACOPF
LUMINA derives three design principles for physics-informed foundation models that balance accuracy, constraint satisfaction, and reliability on topology-transferable ACOPF problems.