Research on profit prediction of backup clamps for conductors and ground wires in transmission line engineering based on machine learning

This study presents an optimized machine learning-driven profit forecasting model tailored for the backup clamps of transmission line conductors and ground wires, critically enhancing the financial planning in power transmission infrastructure. Utilizing a dataset comprising 48 historical samples, the model leverages both random forest and XGBoost regression algorithms, rigorously assessed through ten-fold cross-validation to mitigate overfitting. The XGBoost model, demonstrating marginally enhanced predictive accuracy with relative errors confined within [-20%, +15%], provides a robust framework for budget estimation in transmission line projects, thereby underscoring the efficacy of machine learning in advancing power system economics.