{
  "abstract": "Day-ahead operation of regional natural gas transmission networks requires station demand forecasts and their translation into inventory-preparation references. This study evaluated a sequential forecast-to-dispatch workflow for the Chongqing regional gas network. Six forecasting models were assessed under a leakage-controlled 30-day walk-forward protocol. Across nine routine stations, mean station-level MAPE was 8.61% for SARIMAX, 8.95% for XGBoost, and 9.78% for GRU. The corresponding values were 10.93% for LSTM, 11.63% for BPNN, and 11.77% for CNN-BiLSTM-Attention. Station G was assessed separately as a data-quality boundary case; Station K was excluded because only 62 observations were available. An independent archived 18-day hourly dataset reported reconstruction MAPE of 6.7%−8.1% and peak-timing deviations of 1.0–1.5 h. A retrospective West-pipeline case for 22 April 2023 traced historical station forecasts through weighted aggregation, intermediate rounding, hourly reconstruction, and reduced-order linepack calculation. Its LFFX preparation target and the separate NGXDD and DLFX targets were 1.272, 4.702, and 1.571 × 10⁶ m³, respectively. All three targets passed case-specific deterministic inventory checks. The results support station-specific forecast-model selection and an explicit interface between daily prediction and segment-level inventory preparation.",
  "abstract_shown": true,
  "affiliations": [
    "Surface Engineering Design Center, PetroChina Southwest Oil & Gasfield Company, Chengdu, China",
    "Development and Planning Department, China National Petroleum Corporation, Beijing, China",
    "Gas Field Development and Management Department, PetroChina Southwest Oil & Gasfield Company, Chengdu, China",
    "Gas Distribution and Transmission Management Department, PetroChina Southwest Oil & Gasfield Company, Chengdu, China",
    "Kunlun Digital Technology Co., Ltd., Beijing, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ying Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yong Xiao"
    },
    {
      "affs": [
        2
      ],
      "name": "Zhiping Tang"
    },
    {
      "affs": [
        3
      ],
      "name": "Simin Zhong"
    },
    {
      "affs": [
        4
      ],
      "name": "Chenguang Yong"
    },
    {
      "name": "Yuanyuan Tian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a87hscw0zmavx.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0349981",
  "fulltext_url": null,
  "id": "a87hscw0zmavx",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0349981",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2208|Energy Load and Power Forecasting"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Metadata"
    ],
    [
      "plos",
      "/Computer and information sciences/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Earth sciences/Seasons"
    ],
    [
      "plos",
      "/Engineering and technology/Energy and power/Fuels/Fossil fuels/Natural gas"
    ],
    [
      "plos",
      "/Engineering and technology/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Fuels/Fossil fuels/Natural gas"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Continuum mechanics/Fluid mechanics/Fluid dynamics/Flow rate"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis"
    ]
  ],
  "openalex": "W7220418083",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0349981&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/a87hscw0zmavx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0349981",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0349981"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    }
  ],
  "title": "A forecast-to-dispatch workflow for day-ahead operation of a regional natural gas transmission network based on station-level load prediction and linepack target setting",
  "title_text": "A forecast-to-dispatch workflow for day-ahead operation of a regional natural gas transmission network based on station-level load prediction and linepack target setting",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/a87hscw0zmavx",
  "venue": "PLOS One",
  "versions": []
}