Abstract:

Accurate prediction of transmission line project cost is of great significance to construction quality and cost control. Since the feature dimension in the traditional transmission line project cost prediction is too high and a single prediction model is difficult to fit the complex cost data, a transmission line project cost prediction method is proposed based on embedding dimensionality reduction and ensemble learning. Firstly, the features are sorted with the embedding method and the XGBoost model to screen out the features that have a significant impact on the cost, achieving the data dimensionality reduction. Then the XGBoost, random forest, SVM and other models are integrated to form a two-layer ensemble learning model. Finally, a case study is carried out based on the data of real transmission line projects, and the proposed method is compared with the XGBoost, random forest, SVM, ELM, and BP neural network models. The rusults show that the mean absolute percentage error of the proposed method is within 4%, which is superior to other single model, and is of great value to the research of transmission line project cost control.

Key words: transmission line, cost prediction, ensemble learning, dimensionality reduction, embedding method