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

Ying Liu, Yong Xiao, Zhiping Tang, Simin Zhong, Chenguang Yong and Yuanyuan Tian

PLOS ONE, 2026, vol. 21, issue 10, 1-27

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.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0349981

DOI: 10.1371/journal.pone.0349981

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