{
  "abstract": "Modern power systems face unprecedented challenges from rapid digitalization, electrification of new sectors, and increasing renewable integration. These changes demand real-time operational decisions based on accurate weather forecasting, as traditional dispatch methods cannot handle the speed and complexity of weather-driven variability in renewable generation. This paper thus proposes the Weather-Integrated Neural-Symbolic Dispatch System (WINDS), a novel methodological framework designed to incorporate weather-driven uncertainties into power system dispatch decisions. The WINDS framework combines weather forecasting and neuro-symbolic reasoning, where neural learning methods address weather-related uncertainties and symbolic methods enforce system security constraints, to provide operators with actionable dispatch strategies. Through a detailed set of study cases comprising normal operations, extreme weather events, missing data conditions, and multi-scenario risk analysis, evaluated on both a controlled experiment and measured wind and load data from the Danish DK1 bidding zone, WINDS reduces operational cost by 11.4% to 26.7% compared to static climatology-based dispatch depending on weather volatility, while reducing constraint-violation volume by more than 80% relative to forecast-only dispatch and substantially limiting violations during a measured extreme ramp-down event through adaptive uncertainty margins, with a novel L consistency metric quantifying the alignment between neural predictions and symbolic constraints. WINDS utilizes the strengths of both data-driven and symbolic approaches, enhancing system security and reliability, especially in renewables-rich regions sensitive to weather variability. This work presents a clearly defined methodological framework, and the results provide a proof-of-concept, paving the way for real-world implementation and operational enhancement in weather-sensitive power systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Swaechchha Dahal"
    },
    {
      "name": "Sambeet Mishra"
    },
    {
      "name": "Gunne John Hegglid"
    },
    {
      "name": "Bhupendra Bimal Chhetri"
    },
    {
      "name": "Shailendra Kumar Jha"
    },
    {
      "name": "Thomas Øyvang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/41jsxxgkpy52d.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-73702-5",
  "fulltext_url": null,
  "id": "41jsxxgkpy52d",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-73702-5",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/41jsxxgkpy52d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:36Z",
      "native_id": "10.1038/s41598-026-73702-5",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-73702-5"
    }
  ],
  "subjects": [],
  "title": "A weather integrated neural symbolic dispatch framework for power system operational security",
  "title_text": "A weather integrated neural symbolic dispatch framework for power system operational security",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T00:20:31Z",
  "url": "https://research.draconitic.com/paper/41jsxxgkpy52d",
  "venue": "Scientific Reports",
  "versions": []
}