| Issue |
E3S Web Conf.
Volume 136, 2019
2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2019)
|
|
|---|---|---|
| Article Number | 01028 | |
| Number of page(s) | 5 | |
| Section | Ultra-Low Energy Consumption Building Technology | |
| DOI | https://doi.org/10.1051/e3sconf/201913601028 | |
| Published online | 10 December 2019 | |
Transmission line engineering cost prediction based on principal component analysis and least square support vector machine
1 Economic and Technological Research Institute, State Grid Ningxia Electric Power Co., Ltd., Huyingkangchen No. 1 office building, Baohu Middle Road, Yinchuan City, Ningxia, 750000, China
2 Power Construction Technology and Economic Consultation Center, China Electric Power Enterprise Association, No. 13 Baiguang Road, Xicheng District, Beijing***, 100053, China
* Corresponding author’s e-mail: 2651308061@qq.com
Due to the many factors affecting the cost of transmission line engineering and the lack of mutual independence, it is difficult to predict the cost. Firstly, the principal component analysis is used to process the original indicator data, eliminating the correlation between the original indicators and extracting the potential comprehensive independent indicators. Then, the new indicator is used as the input set to construct the predictive learning model based on the least squares support vector machine, and the predicted output and the actual value are compared and analyzed. The results show that the model can achieve the desired prediction effect in the case of small samples.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.