arXiv Machine Learning
5d ago

Bayesian Distribution System State Estimation with Flow Matching

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.

By Nan Lin, Peter Palensky, Pedro Vergara Barrios
arXiv Machine Learning
Oct 16

Physics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference

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.

By Vlad Alexandru S\u{a}nduleac, Shahariya Rasheed Rayamarakkarveettil Puthanpurayil, Noman Khan, Remus Teodorescu
arXiv Machine Learning
Sep 17

Multi-Appliance Non-Intrusive Load Monitoring via Label-Preserving Aggregate Recomposition and Prediction Consistency

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.

By Jiangfeng Liu, Yanfang Fan