The paper presents a modular, scalable framework for estimating the probability of failure (PoF) of power‑line assets using geospatial machine learning. It integrates diverse environmental predictors—topography, vegetation indices, lightning climatology, proximity features, and operational records—to model vegetation‑ and lightning‑related failure modes. The architecture is designed to be computationally efficient, easily extensible to new data sources, and suitable for large‑scale utility deployment, enabling asset‑level risk stratification for inspection and resilience planning.
By Artur Sokolovsky, Bhavik Merai, Moe Jafari, Muen Chen
This study evaluates large language models (LLMs) for predicting weather‑related forced outage risk in a distribution grid using a zero‑shot approach without labeled training data. The task is framed as binary severity classification over 3h, 6h, and 12h horizons, leveraging six years of outage records and high‑resolution weather data from central Texas. Four zero‑shot LLMs are compared to two supervised classifiers under two input settings—current weather observations and forecast data—showing that supervised models lead on macro‑F1 and precision, while newer LLMs achieve competitive scores and offer complementary strengths in reasoning and geographic scalability.
By Christos Petridis, Zoran Obradovic, Mladen Kezunovic
arXiv:2510. 00831v2 Announce Type: replace Abstract: The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protection schemes and motivates the use of machine learning for protection tasks.
By Julian Oelhaf, Georg Kordowich, Changhun Kim, Paula Andrea P\'erez-Toro, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
arXiv:2609.16744v1 Announce Type: new
Abstract: The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery...
By Julian Oelhaf, Georg Kordowich, Paula Andrea P\'erez-Toro, Tom\'as Arias-Vergara, Andreas Maier, Johann J\"ager, Siming Bayer
The paper introduces Probabilistic Bias Correction (PBC), a machine learning framework that learns to correct historical probabilistic forecasts, thereby reducing systematic errors in subseasonal weather predictions. Applied to leading dynamical and AI models from ECMWF, PBC doubles the AI system’s modest subseasonal skill and improves the operationally-debiased dynamical model for most pressure, temperature, and precipitation targets. In ECMWF’s 2025 real‑time forecasting competition, PBC’s global forecasts ranked first across all weather variables and lead times, outperforming multiple operational and ensemble models.
By Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen, Haiyu Dong, Zekun Ni, Jeremy Berman, Genevieve Flaspohler, Alex Lu, Jakob Schloer, Joshua Talib, Jonathan A. Weyn, Lester Mackey
arXiv:2608. 02088v1 Announce Type: new Abstract: Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records.
By Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah, Stephanie Gauthier, Masood Nazari
Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current forecasting methods require significant manual effort, often lack uncertainty estimation and proper peak prediction, and they are often not adequately evaluated in terms of grid requirements.
arXiv:2604. 16238v2 Announce Type: replace Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes.
By Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen, Haiyu Dong, Zekun Ni, Jeremy Berman, Genevieve Flaspohler, Alex Lu, Jakob Schloer, Joshua Talib, Jonathan A. Weyn, Lester Mackey
arXiv:2608.24665v1 Announce Type: new
Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whe...
By Yamil Essus, Ranga Raju Vatsavai, Benjamin Rachunok
arXiv:2607. 01966v1 Announce Type: new Abstract: Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation.
By Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber, Oliver Neumann, Cheewan Phatthanakhuha, Oliver Resch, Ralf Mikut, Veit Hagenmeyer
The paper introduces CloudCast v2, a machine‑learning model that forecasts 12‑hour cloud cover from observation‑based initial conditions. It is first trained on the Copernicus European Regional Reanalysis to capture cloud‑evolution dynamics, then adapted to satellite‑derived cloud fields using conditional flow matching. Compared to its predecessor, CloudCast v2 reduces mean absolute error by 10% over 1–12 h and surpasses it in spatial skill after 3–6 h, demonstrating that observation‑initialized forecasts can extend beyond the typical 1–3‑hour nowcasting window while preserving spatial detail.
arXiv:2603. 12828v2 Announce Type: replace-cross Abstract: This paper addresses a missing capability in infrastructure resilience: turning fast, global AI weather forecasts into asset-scale, actionable risk intelligence.
By You Wu, Zhenguo Wang, Naiyu Wang