Research On Overhead Line Engineering Cost Prediction Based On Pca-Lssvm Model

In recent years, the investment projects of overhead line engineering increase year by year. Establishing scientific cost prediction concept and optimizing cost prediction method can improve the investment utilization efficiency. Based on the actual cost data of 110kV overhead line project, this paper extracts the principal component factor through principal component analysis and eliminates the correlation between the original indicators. Then, the training sample is input into the least-squares support vector machine model to build a learning network. Finally, the predicted value of the model is compared with the actual cost level for analysis. The prediction results show that the average error rate is less than 5%, indicating that the PA-LSSVM model constructed in this paper can effectively predict the overhead line engineering cost.