arXiv Machine Learning

A Systematic Evaluation of Current Architectures in Wind Power Forecasting

arXiv:2606. 02849v1 Announce Type: new Abstract: Interval wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it accounts for the inherent uncertainty of wind resources.

arXiv Machine Learning

Jul 24

SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting

arXiv:2607. 20587v1 Announce Type: cross Abstract: Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market volatility, and weather-dependent generation.

By Hang Ye, Xinyan Jiang, Yuedong Shi, Yangxin Zhu, Jianming Wei, Tian Zheng, Xiaoying Zheng, Yongxin Zhu

safety

arXiv Machine Learning

Jul 14

Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles

arXiv:2602. 13010v2 Announce Type: replace Abstract: Accurate production forecasts are essential for the integration of renewable energy sources into the power grid.

By Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten, Ann Now\'e, Jan Helsen

diffusion benchmarks

arXiv Machine Learning

Jun 3

Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.

By Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar, Pascal Van Hentenryck

arXiv Machine Learning

Jul 31

Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit

arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.

By Zhixian Wang, Leandro Von Krannichfeldt, Qingsong Wen, Chaoli Zhang, Liang Sun, Shirui Pan, Yi Wang

llms agents benchmarks

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 9

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.

By Jiahui Huang, Ao Luo, Lei Liu, Hongwei Zhao, Tengyuan Liu, Ruibo Guo, Bo Wang, Zhao Wang, Bin Li

rag

arXiv AI

Jun 24

EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting

arXiv:2512. 15067v4 Announce Type: replace-cross Abstract: The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to ensure ongoing compliance, assess potential health impacts, and support efficient network planning.

By Zijiang Yan, Yixiang Huang, Jianhua Pei, Hina Tabassum, Luca Chiaraviglio

diffusion

arXiv Machine Learning

Jun 10

Stochastic weather generators for high-frequency wind vector time series

arXiv:2606. 09941v1 Announce Type: cross Abstract: Surface winds can vary substantially from one minute to the next, so there is scope for studying its variation on this fine time scale.

By Mingshi Cui, Kevin Eng, Justin T. Greene, Zern Ke, Abolfazl Sodagartojgi, Zhiqiu Xia, Gemma E. Moran, Michael L. Stein

efficiency

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)

arXiv AI

Jun 3

Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels

arXiv:2606. 02886v1 Announce Type: cross Abstract: Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estimates, a critical gap for high-stakes decisions during extreme weather events.

By Jose Marie Antonio Mi\~noza, Rex Gregor Laylo, Sebastian C. Iba\~nez

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

Jun 10

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting

arXiv:2606. 10592v1 Announce Type: new Abstract: Time series forecasting often suffers from over-smoothing, especially when future dynamics are multi-modal.

By Xingyu Zhang, Jingyao Wang, Xin Yu, Zeen Song, Jianqi Zhang, Changwen Zheng, Wenwen Qiang

multimodal benchmarks

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