Fast Probabilistic Wind Power Forecasting via One-Step Consistency Distillation and Memory-Efficient CRPS Optimization

Source abstract

Grid operators scheduling wind power act on the width of the predicted distribution, and diffusion-based forecasters have reported strong accuracy on wind benchmarks. That accuracy can come from a sampling chain of a hundred or more evaluations per member, with reported implementations incurring multi-second latency per farm against a dispatch cycle of minutes, while a one-step student distilled from such a chain can concentrate its members into a spread narrower than its own error. This paper introduces Consist-Wind, a one-step probabilistic wind power forecaster. A frozen point backbone supplies the conditioning context; a teacher diffusion model on numerical weather prediction (NWP) residuals is distilled into a one-step consistency student, one function evaluation per member, and a stabilized shifted-logit warp maps generated members to the physical power range. A fourth phase calibrates the student by training its ensemble against the continuous ranked probability score (CRPS), which recovers the spread and raises the accuracy of the one-step forecast. Evaluated at every update, its pairwise form governs the training footprint, so the sorted-rank form of the CRPS is used, quasilinear in time and linear in memory. The cost of calibration is decoupled from the ensemble width by a scalable ensemble calibration principle, with the objective on order statistics and query-chunked attention (QCA) inside the kernel launch limit, so the whole pipeline trains and runs on a single device with limited compute. In our experiments on GEFCom2014-W, SDWPF, and GEFCom2012-W, Consist-Wind achieves the lowest CRPS, 3.1% below the strongest reproduced baseline, reduces the calibration error of the distilled student by 45%, issues a forecast in 0.03 ms for a speedup of about 66× relative to the reproduced multi-step reference, and reduces the memory required to evaluate the pairwise penalty term by 98%, admitting 500-member calibration ensembles on the evaluation device. Distributional forecasts of this accuracy, trained within a limited-compute budget, bring uncertainty-aware scheduling within reach of operators on commodity hardware.