{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "School of Information and Intelligent Science, Donghua University, Shanghai 201620, China",
    "High Speed Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chao He"
    },
    {
      "affs": [
        1
      ],
      "name": "Fangqi Zhou"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Hao Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Zijian Wang",
      "orcid": "0000-0002-8147-5109"
    },
    {
      "affs": [
        1
      ],
      "name": "Jianbo Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cknhzpgf0qn9a.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203687",
  "fulltext_url": null,
  "id": "cknhzpgf0qn9a",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3687",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2208|Energy Load and Power Forecasting"
    ]
  ],
  "openalex": "W7221551315",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/cknhzpgf0qn9a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203687",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3687"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Fast Probabilistic Wind Power Forecasting via One-Step Consistency Distillation and Memory-Efficient CRPS Optimization",
  "title_text": "Fast Probabilistic Wind Power Forecasting via One-Step Consistency Distillation and Memory-Efficient CRPS Optimization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/cknhzpgf0qn9a",
  "venue": "Mathematics",
  "versions": []
}