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

Abstract: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 integrates physics-informed generation priors with data-driven state-aware corrections. UniWind first employs a Physical Prior Estimator to construct a site-calibrated physical prior by combining a site-conditioned monotone warp with a shared physical power curve. It further applies a physical upper-bound constraint to shape this prior as a soft envelope of available wind power generation. UniWind then uses a Latent State Encoder to construct latent power-state representations from historical prior-power discrepancies and future meteorological context, and transforms the physical prior into final power forecasts through a State-aware Power Corrector with pseudo-label-guided routing and bounded expert correction. Experiments on 24 real-world datasets demonstrate UniWind's strong forecasting accuracy, robustness, and generalization across full-shot, cross-farm zero-shot, and cross-region zero-shot settings.

Machine Learning (cs.LG)

arXiv:2607.01670 [cs.LG]

(or arXiv:2607.01670v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.01670 Focus to learn more arXiv-issued DOI via DataCite

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