arXiv Machine Learning

Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curves

arXiv:2607. 00051v1 Announce Type: cross Abstract: Accurate modeling of wind turbine power curves is crucial for optimal wind farm operation.

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 Machine Learning

Jul 14

TSCoNet: A Two-Stage Copula CNN-LSTM for Uncertainty-Aware Spatio-Temporal Forecasting

arXiv:2607. 10410v1 Announce Type: cross Abstract: Reliable forecasting of several interrelated environmental variables - such as regional precipitation and temperature, or other correlated geophysical fields - across many locations calls for accurate predictions accompanied by trustworthy statements of their uncertainty.

By Jongwook Kim, Jong-Min Kim

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

Jul 23

Domain-Adapted Power Curve for Cross-Farm Applications

arXiv:2607. 19744v1 Announce Type: cross Abstract: The wind energy industry relies on accurate power curve models to make power forecast, evaluate turbine performance, quantify upgrade, or support site-planning decisions.

By Ahmadreza Chokhachian, V. Roshan Joseph, Yu Ding

fine-tuning

arXiv AI

Aug 11

Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance

arXiv:2605. 18793v2 Announce Type: replace-cross Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring.

By Jing Chen, Shixiang Pan, Yujie Fan, Haocheng Ye, Haitao Xu, Wenqiang Xu

rag safety

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 AI

Aug 11

AirFlow: Context Preserving and Multi-Rate State Modeling for Air Quality Forecasting

arXiv:2608. 09775v1 Announce Type: new Abstract: Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels differ in periodicity and distribution drift, while their concentration trajectories contain both multi-scale dependencies and rapid changes.

By Fan Yang, Nan Chen, Yijie Dong, Yuchen Zhang, Wei Zhang

benchmarks data-engineering

arXiv AI

Jun 9

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

arXiv:2310. 10196v3 Announce Type: replace-cross Abstract: Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications.

By Ming Jin, Yaxuan Kong, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong, Shirui Pan, Qingsong Wen

llms

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 Machine Learning

Jun 15

Nonlocal Bayesian Modeling of Continuous Spatio-Temporal Dynamics

arXiv:2606. 14313v1 Announce Type: cross Abstract: Real-world spatio-temporal forecasting must handle irregular time points, spatially sparse observations, and the need for uncertainty quantification.

By Jaeyeong Lee, Heeyoung Kim

arXiv Machine Learning

6d ago

A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression

arXiv:2608. 11917v1 Announce Type: new Abstract: Multi-output Gaussian process regression scales cubically in the number of observations times outputs, and dense kernel-matrix methods need bespoke handling whenever different outputs are observed at different inputs.

By Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko, \.Ismail \c{S}en\"oz, Wouter M. Kouw

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

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