Interval-Balanced Machine Learning for Small-Sample Cost Forecasting of Overhead Transmission Line Construction Projects

Reliable early-stage cost forecasts support investment planning and budget review for power infrastructure construction. However, historical project datasets are often limited and unevenly distributed across cost ranges, which may cause models selected according to aggregate error alone to perform inconsistently for projects at different cost levels. This study developed an interval-balanced particle swarm optimization–least squares support vector machine (PSO-LSSVM) approach for forecasting the construction costs of overhead transmission line projects. The hyperparameter-selection objective combined global prediction error, the dispersion of interval-specific errors, and the excess error of the worst-performing interval. Cost intervals were derived from the training responses through one-dimensional clustering, with the number of intervals selected using the silhouette coefficient. A leakage-controlled nested cross-validation procedure was used to separate data preprocessing, interval construction, hyperparameter selection, and out-of-sample evaluation. The approach was evaluated using 440 completed projects and compared with ridge regression, random forest, support vector regression, conventional LSSVM, and conventional PSO-LSSVM. Conventional PSO-LSSVM achieved the best aggregate performance, including a MAPE of 9.63% and an RMSE of CNY 32.21 × 104. The proposed method achieved a similar MAPE of 9.80%, although its RMSE increased to CNY 39.88 × 104. It produced the lowest dispersion of interval-specific MAPE values and reduced the worst-interval MAPE from 13.60% to 12.49% relative to conventional PSO-LSSVM. The proposed method also achieved the lowest MAPE in the low- and high-cost intervals and reduced the number of projects with APE above 30% from 28 to 20 relative to conventional PSO-LSSVM. Since projects are budgeted and managed individually, these improvements address the practical need to keep individual project forecasts within an acceptable error tolerance.