Research on Cost Forecasting Model of Power Line Engineering Based on BP Neural Network
- 10.2991/meici-18.2018.70How to use a DOI?
- Engineering cost; Neural network; Power line; Prediction model
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.
- © 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/).
Cite this article
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 -