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

DOI

10.2991/meici-18.2018.70How to use a DOI?

Keywords

Engineering cost; Neural network; Power line; Prediction model

Abstract

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dan Chen
AU  - Weiya Guan
AU  - Jingyi Wang
AU  - Hua Zhang
AU  - Delv Zhu
AU  - Hao Zhen
PY  - 2018/12
DA  - 2018/12
TI  - Research on Cost Forecasting Model of Power Line Engineering Based on BP Neural Network
BT  - Proceedings of the 2018 8th International Conference on Management, Education and Information (MEICI 2018)
PB  - Atlantis Press
SP  - 359
EP  - 364
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-18.2018.70
DO  - 10.2991/meici-18.2018.70
ID  - Chen2018/12
ER  -