arXiv AI

Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting

arXiv:2606. 08630v1 Announce Type: cross Abstract: Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates.

arXiv AI

Jul 21

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.

By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair

benchmarks

arXiv Machine Learning

Jun 18

Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting

arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.

By Tao Han, Zhibin Wen, Zhenghao Chen, Dazhao Du, Song Guo, Lei Bai

llms benchmarks safety

arXiv Machine Learning

Jul 3

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing

arXiv:2607. 01670v1 Announce Type: new Abstract: Day-ahead wind power forecasting is essential for cost-effective power-system operation.

By Ronghui Xu, Tongxin Wu, Guozhen Zhang, Yihan Li, Chenjuan Guo, Bin Yang, Yong Li

rag

arXiv Machine Learning

Jul 21

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

arXiv:2607. 17511v1 Announce Type: new Abstract: Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains.

By Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, Yanchang Zhao, Yun Chen

fine-tuning

arXiv Machine Learning

6d ago

Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.

By Pedro Sousa (Department of Computer Science, University of Cambridge), Will Tebbutt (Department of Engineering, University of Cambridge), Sadiq Jaffer (Department of Computer Science, University of Cambridge), Robin Young (Department of Computer Science, University of Cambridge), Anil Madhavapeddy (Department of Computer Science, University of Cambridge), Richard E. Turner (Department of Engineering, University of Cambridge)

rag

arXiv Machine Learning

Jul 30

Skillful forecasting of offshore winds from satellite scatterometer constellations

arXiv:2607. 27152v1 Announce Type: new Abstract: Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy.

By Francesco Pinto, Luca Lanzilao, Paco Lopez Dekker, Angela Meyer

arXiv Machine Learning

Jun 19

When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting

arXiv:2606. 19363v1 Announce Type: new Abstract: The deployment of Time-Series Foundation Models (TSFMs) in physical sciences is hindered by a critical trade-off: while these models encode rich, universal temporal dynamics, they suffer from severe distributional misalignment when applied zero-shot to specific scientific domains, and their computational cost prohibits deployment in edge-computing sensor networks.

By Rupasree Dey, Abdul Matin, Nathan Orwick, Yao Zhang, Shrideep Pallickara, Sangmi Lee Pallickara

efficiency

arXiv Machine Learning

Jun 8

Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting

arXiv:2606. 07457v1 Announce Type: new Abstract: At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters.

By Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi, Emanuele Frontoni, Riccardo Rosati

arXiv AI

Jul 21

Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction

arXiv:2607. 17095v1 Announce Type: new Abstract: Accurate short-term wind power forecasting is essential for grid stability and operational planning, yet remains challenging due to the complex interactions between atmospheric conditions and turbine dynamics.

By Shiyuan Piao, Fan Zehui, Yang Liu, Hong Cheng, Juepeng Zheng, Jie Zhou, Fugee Tsung

multimodal benchmarks

arXiv AI

Jun 2

U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster

arXiv:2604. 09041v2 Announce Type: replace-cross Abstract: AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets, creating a high barrier to entry.

By Salva R\"uhling Cachay, Duncan Watson-Parris, Rose Yu

diffusion fine-tuning efficiency benchmarks

arXiv Machine Learning

Aug 7

Timestep-Conditioned Transformers for Global Weather Forecasting

arXiv:2608. 06241v1 Announce Type: new Abstract: Existing machine-learning weather forecasting models rely on predetermined and fixed autoregressive timesteps.

By Sam Levang, Fran Bartolic, Ty Dickinson, Chase Dwelle, Paulius Rauba, Viktor Cikojevic

llms benchmarks

arXiv Machine Learning

Jul 14

Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting

arXiv:2607. 11470v1 Announce Type: cross Abstract: Reliable uncertainty quantification is essential for integrating solar and wind generation into modern power systems, where operators must weigh risk rather than act on point forecasts alone.

By Shreedhar Gangwar (B. R. Ambedkar National Institute of Technology, Jalandhar, India), Abhinav Bains (B. R. Ambedkar National Institute of Technology, Jalandhar, India), Banalaxmi Brahma (B. R. Ambedkar National Institute of Technology, Jalandhar, India)

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