Statistical Cost Anomaly Screening and Explanation for Preliminary Design Estimates of Power Grid Substation Projects

Reliable review of preliminary design estimates is important for cost control in power grid substation projects, yet fixed thresholds have limited ability to reflect project-specific engineering conditions. This study develops a multidimensional cost-deviation screening framework integrating CatBoost, Conformalized Quantile Regression (CQR), Cost Structure family-wise calibration, and SHapley Additive exPlanations (SHAP)feature attribution. Total Cost, Unit Cost, and Cost Structure are evaluated jointly, with CQR providing project-specific marginal prediction intervals and an economic-exposure-weighted calibration controlling simultaneous screening across the five Cost Structure components. SHAP is subsequently used to provide feature-level interpretation of screened projects. The framework was evaluated using 906 substation projects from 27 provincial grid companies in China, including 761 projects for development and 145 for held-out evaluation. CatBoost achieved R2 values of 0.9363 for Total Cost and 0.8899 for Unit Cost. After Cost Structure family-wise calibration, 22 held-out projects (15.17%) received review flags. Incorporating Cost Structure increased the number of flagged projects from 15 to 22, identifying seven additional projects not flaggedby Total Cost or Unit Cost alone. The results demonstrate competitive predictive performance and the incremental screening value of Cost Structure, although prediction-interval efficiency remained target dependent. The proposed framework provides an uncertainty-aware and interpretable approach for prioritizing preliminary-design cost review.