State Estimation as In-context Tabular Prediction
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2610.20462v1 Announce Type: new Abstract: Foundation models have shown strong generalization across scientific domains, but their use in power systems remains largely task-specific. We investig...
The paper introduces PoSE, a Bayesian posterior sampling method for distribution system state estimation that learns the distribution of substation injections via a neural network trained with flow matching. Unlike weighted least squares or supervised deep learning, PoSE preserves the full Bayesian structure and ensures every posterior sample satisfies the power flow equations. Experiments on IEEE 33‑bus and Iowa 240‑bus feeders show PoSE reduces injection error by up to 7.5% and provides more reliable uncertainty estimates than Gaussian priors or supervised estimators.
The paper presents a physics‑aware distillation of a large Grid Foundation Model (GridFM) for real‑time edge inference. A 20.1 M‑parameter, 12‑layer teacher is compressed into a 0.62 M‑parameter, 6‑layer student, achieving a 32.5× reduction in parameters and halving message‑passing depth while improving accuracy on active‑power dispatch by up to 48 % and always outperforming the teacher on reactive‑power dispatch. The distillation objective combines masked power‑flow loss with output, physics‑residual, latent‑state, and attention transfer, though the physics‑residual term proved detrimental in this setting.
Grid foundation models (GridFMs) are graph transformers pretrained on a masked power-flow objective, and evaluate power flow orders of magnitude faster than an iterative solver. Their size, however, t...
The paper introduces a new NILM approach that uses label‑preserving aggregate recomposition and prediction consistency to improve appliance‑level power estimation. By recomposing aggregate windows with only the residual background changed, the method preserves target appliance signals while exposing a new supervisory signal. Experiments on REDD, UK‑DALE, and REFIT show reduced mean absolute error for multiple appliances without adding inference‑time complexity.
arXiv:2607. 16031v1 Announce Type: cross Abstract: Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning.