arXiv Machine Learning
WPBench: A Comprehensive Benchmark for Wind Power Forecasting
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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 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.
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.
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.
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.
arXiv Machine Learning
Jun 3
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.