DOI: 10.11648/j.sr.20261404.24 ISSN: 2329-0927

Research on Dynamic Cost Prediction of Power Grid Technical Renovation Projects Based on Improved Transformer

Luo Yi
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Cost prediction for power grid technical renovation projects serves as a critical component of investment decision-making and optimal resource allocation for power enterprises, and its results directly affect investment control, budget formulation and risk assessment. To address the limitations of conventional methods, which struggle to capture the dynamic evolution of costs throughout the project life cycle and overlook the differences in cost characteristics across various construction phases, this paper proposes a dynamic cost prediction model based on the Gated Multi-Stage Transformer (GMST). The model divides technical renovation projects into three phases: planning, implementation and settlement. Separate Transformer encoders are established to extract temporal features of each phase, and a learnable gating network is introduced to adaptively aggregate multi-stage prediction information and realize dynamic weighting of features from different phases. Experiments on datasets of 1,876 real-world projects demonstrate that the GMST achieves an MAE of 123,700 CNY, an RMSE of 185,400 CNY and a MAPE of 5.82%, outperforming vanilla Transformer, LSTM and XGBoost by a notable margin. Ablation experiments verify the synergistic effect of multi-stage encoding and gated fusion. Phase-wise accuracy analysis reveals that the prediction error gradually converges from 9.21% to 5.82% as the project proceeds, indicating that the proposed model is suitable for the progressive cost estimation scenario of technical renovation projects..

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