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.
arXiv:2610.02248v1 Announce Type: new Abstract: Operational land surface forecasting systems built on Mamba-family Structured State Space Models absorb non-stationary confounding events (unrecorded i...
arXiv:2610. 12232v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods for time-series forecasting update a deployed model, or a small adapter around it, from incoming ground truth.
The paper introduces TEGER, a residual covariance model that updates a forecaster’s joint predictive uncertainty at test time using closed‑form Gaussian conditioning on recent residuals and an exponential moving‑average volatility term. It leverages a fixed sensor graph to encode spatial correlations without retraining the underlying forecasting backbone. Experiments on four traffic datasets show significant improvements in 60‑minute CRPS, and applying TEGER to a frozen foundation model (Chronos) reduces CRPS from 0.1798 to 0.1736.
arXiv:2601.01480v3 Announce Type: replace-cross Abstract: Traffic forecasting systems rely on fixed sensor networks that frequently exhibit contiguous blackouts. Such outages are usually treated as i...
arXiv:2608.20024v2 Announce Type: replace Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Forecasting models trained on historical da...
The study evaluates how well time‑series foundation models (TSFMs) can be adapted for electrical load forecasting. It compares pretrained inference, full fine‑tuning, and low‑rank adaptation (LoRA) across four load aggregation levels, using MOIRAI 1.0 Large as the main backbone and Chronos‑2 as a secondary model. LoRA achieves a 7.5% average NMAE reduction on MOIRAI while updating only about 1% of the parameters, offering a strong accuracy‑efficiency trade‑off, though benefits vary in out‑of‑distribution scenarios.