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Research on dynamic cost prediction method for power technology upgrading projects based on improved transformer
Qi Shi1, Yue Le2, Chunjie Gu3
1Production Technology Department, State Grid East China Branch, Shanghai, 200120, China. shiqisgcc@163.com.
Scientific Reports
|June 23, 2026
Summary
This study introduces an advanced Transformer model for accurate power grid technical renovation (PGTR) cost prediction. The new framework significantly improves prediction accuracy over existing methods, aiding project management.
Area of Science:
- Electrical Engineering
- Data Science
- Artificial Intelligence
Background:
- Accurate cost prediction is vital for managing power grid technical renovation (PGTR) projects.
- Traditional methods fail to capture complex dynamics in renovation cost data.
Purpose of the Study:
- To develop an improved Transformer-based framework for accurate PGTR cost prediction.
- To address the nonlinear, multi-scale, and time-varying nature of renovation cost data.
Main Methods:
- Proposed an MS-DW-Transformer framework combining multi-scale convolutional encoding, dynamic weight allocation, and auxiliary loss.
- Tested the framework on 1,362 completed PGTR projects.
- Compared performance against classical and recent time-series Transformer models.
Main Results:
- Achieved a mean MAPE of 6.83% and R² of 0.937 on the test set.
- Demonstrated statistically significant improvements over baseline models.
- Provided detailed analyses of model interpretability and sensitivity.
Conclusions:
- The MS-DW-Transformer framework offers superior performance for PGTR cost prediction.
- The model's design effectively captures complex data dynamics.
- Further research is needed to assess generalizability beyond the studied dataset.
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