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<p>Accurate cost prediction is critical for the effective management of power grid technical renovation (PGTR) projects, yet conventional methods struggle to capture the nonlinear, multi-scale, and time-varying dynamics inherent in renovation cost data. This paper proposes an improved Transformer-based framework, MS-DW-Transformer, that combines parallel multi-scale convolutional encoding with a gated dynamic weight allocation mechanism and a trend-consistency auxiliary loss. We test the framework on 1,362 completed PGTR projects from a provincial grid corporation in eastern China, contrasting its behavior against classical baselines (BPNN, SVR, LSTM, GRU, vanilla Transformer) as well as more recent time-series Transformers (Informer, Autoformer, PatchTST, Temporal Fusion Transformer) and a graph-based learner. Across five independent runs the proposed model attains a mean MAPE of 6.83% (± 0.11) and an R² of 0.937 on the held-out test set, with paired-sample significance tests confirming that the gains over the strongest baselines are not artefacts of random variation. We additionally provide gate-activation trajectories, SHAP-based feature attribution, and sensitivity analyses for the window length and loss-weight coefficients, all of which back up — though never fully resolve — the case for the proposed design. The work, we should add, is calibrated to a single-province dataset; the generalizability claims should be read accordingly.</p>

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Research on dynamic cost prediction method for power technology upgrading projects based on improved transformer neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Qi Shi,
  • Yue Le,
  • Chunjie Gu,
  • Yaming Geng

摘要

Accurate cost prediction is critical for the effective management of power grid technical renovation (PGTR) projects, yet conventional methods struggle to capture the nonlinear, multi-scale, and time-varying dynamics inherent in renovation cost data. This paper proposes an improved Transformer-based framework, MS-DW-Transformer, that combines parallel multi-scale convolutional encoding with a gated dynamic weight allocation mechanism and a trend-consistency auxiliary loss. We test the framework on 1,362 completed PGTR projects from a provincial grid corporation in eastern China, contrasting its behavior against classical baselines (BPNN, SVR, LSTM, GRU, vanilla Transformer) as well as more recent time-series Transformers (Informer, Autoformer, PatchTST, Temporal Fusion Transformer) and a graph-based learner. Across five independent runs the proposed model attains a mean MAPE of 6.83% (± 0.11) and an R² of 0.937 on the held-out test set, with paired-sample significance tests confirming that the gains over the strongest baselines are not artefacts of random variation. We additionally provide gate-activation trajectories, SHAP-based feature attribution, and sensitivity analyses for the window length and loss-weight coefficients, all of which back up — though never fully resolve — the case for the proposed design. The work, we should add, is calibrated to a single-province dataset; the generalizability claims should be read accordingly.