Research on Cost Forecasting Model of Power Line Engineering Based on BP Neural Network

被引：0

作者：

Chen, Dan ^{[1
]}

Guan, Weiya ^{[1
]}

Wang, Jingyi ^{[1
]}

Zhang, Hua ^{[1
]}

Zhu, Delv ^{[1
]}

Zhen, Hao ^{[2
]}

机构：

[1] State Grid Jiangsu Elect Power Co Ltd, Econ Res Inst, Nanjing 210008, Jiangsu, Peoples R China

[2] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China

来源：

PROCEEDINGS OF THE 2018 8TH INTERNATIONAL CONFERENCE ON MANAGEMENT, EDUCATION AND INFORMATION (MEICI 2018) | 2018年 / 163卷

关键词：

Engineering cost; Neural network; Power line; Prediction model;

D O I：

暂无

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

In view of the large number of different types of power line engineering projects and the large deviation of cost estimation, how to use a small amount of engineering information to quickly and accurately predict and compare the cost of each project has become a concern. In order to solve this problem, this paper builds a power line engineering cost prediction model based on the BP neural network algorithm of 3 layers 8-12-1 network structure. By analyzing the influencing factors affecting the construction cost of transmission lines, eight indicators are extracted as input factors and the power line engineering cost is used as the output layer factor. In this paper, the BP neural network prediction model is trained and verified by actual engineering data. The experimental results show that the model can estimate the power engineering cost accurately, and can be used for pre-decision phase of the project.

引用

页码：359 / 364

页数：6

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