The study evaluates how forecast lead time influences the effectiveness of outage predictions for crew deployment using AI weather models GraphCast and FourCastNet. It finds that the ability to capture outage burden decreases with longer lead times, with a notable drop between three and five days. Early deployment with all requested crews outperforms late deployment with fewer crews, and the study also examines the impact of antecedent land surface predictors and calibration of prediction intervals for tropical cyclone events.
By Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohamad Alipour
The paper introduces a data‑onboarding pipeline that detects and repairs defects in building meter data before training machine‑learning demand‑forecasting models. Controlled experiments on hourly electricity data from twelve U.S. buildings show that the pipeline restores forecast accuracy to clean‑data levels, even when defects affect up to 1.6% of training rows, and works across multiple models and horizons. The study also documents a first‑version over‑cleaning issue, its cause, and a per‑building calibration that corrects it, with all results reproducible from publicly pinned inputs.
By Yixuan Liang
arXiv:2610.31959v1 Announce Type: cross
Abstract: Short-term building load forecasting remains challenging because load patterns can change rapidly with weather and operating conditions. Data-driven...
By Shiqiao Zhou, Huanbo Lyu, Holger Sch\"oner, Zipeng Wu, Jessica Deuschel, Freya Wentzel, IAG Wilson, Shuo Wang
The paper proposes a shift from predictive modeling to descriptive provider behavior profiles for fraud, waste, and abuse (FWA) review. By decomposing billed revenue into provider scale and procedure composition, the authors construct lineage‑aware profiles that capture scale history, code lineage, and clinical family shares. In a large Medicare audit, these simple, interpretable descriptions outperform complex forecasts and improve recall for high‑cost rare events, while an optional semantic factorization adds context without inferring intent.
By Yubin Park, Evan Brociner
The paper introduces a lightweight pre‑encoder gate for Transformer‑based time‑series forecasters, which assigns sigmoid scores to covariate representations before they enter the encoder. The gate is evaluated as a plug‑in for models such as TimeXer, iTransformer, and PatchTST on datasets including ETTm1, ETTm2, Traffic, Energy, and ILI, showing competitive performance and the ability to regulate covariate admission via a usage penalty. Experiments also explore gate placement, initialization, and feature importance using VIF‑informed permutation diagnostics.
By Hongkai Zhuang, Tao Huang, Chen Hou
arXiv:2508. 04780v2 Announce Type: replace-cross Abstract: The increasing frequency of extreme weather events, such as hurricanes, highlights the urgent need for efficient and equitable power system restoration.
By Lin Jiang, Dahai Yu, Rongchao Xu, Tian Tang, Guang Wang