Combined Use of Support Vector Machine and Extreme Gradient Boosting System for Cost Prediction of Ultra High Voltage Transmission Projects

Abstract

The cost prediction of an ultra-high voltage (UHV) power transmission project represents an important part of management and control of the project. In order to improve the prediction accuracy of an UHV transmission project, a combinational forecasting method is applied. An UHV power transmission project could be decomposed into several subprojects depending on the characteristics of the project, and the cost prediction could be made first for each subproject and then integrated. The principal component analysis and expertise of domain experts are first combined to get the key factors of the subprojects. Then the genetic algorithm based support vector machine and extreme gradient boosting system are used for cost prediction respectively. The Shapley value theory in game theory is next employed to determine the weights of combinatorial forecasting so as to attain an appropriate combinatorial forecasting model. Finally, an actual UHV project is employed to demonstrate the prediction accuracy of the presented combinatorial forecasting model.