UniWind: Day-Ahead Wind Power Forecasting via Physics-Informed State Routing
Accurate day-ahead wind power forecasting is essential for cost-effective power-system operation. Wind power generation is primarily driven by future meteorological conditions while retaining temporal dependencies in power generation. In practice, observed wind-farm power reflects both meteorology-driven generation potential and deviations associated with local environmental and operational factors. Existing physical models provide useful constraints but adapt poorly across wind farms, whereas data-driven models can capture rich correlations but do not explicitly structure these two sources of variation. To bridge this gap, we propose UniWind, a wind power forecasting framework that integ...
| Authors | Ronghui Xu, Tongxin Wu, Guozhen Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 July 2026 |
| Last updated at source | 15 October 2026 |
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