湖南电力 ›› 2023, Vol. 43 ›› Issue (2): 108-111.doi: 10.3969/j.issn.1008-0198.2023.02.018

• 经验与探讨 • 上一篇    下一篇

基于大数据的输变电工程造价趋势预测

邓怡卿, 孙斌, 李晓兵, 李丹, 王嵘婧   

  1. 国网陕西省电力有限公司经济技术研究院,陕西 西安 710075
  • 收稿日期:2022-11-29 修回日期:2023-01-03 出版日期:2023-04-25 发布日期:2023-05-06
  • 作者简介:邓怡卿(1986),女,陕西铜川人,硕士,高级工程师,通信作者,主要从事电力技术经济工作。

Prediction of Power Transmission Project Cost Trend Based on Big Data

DENG Yiqing, SUN Bin, LI Xiaobing, LI Dan, WANG Rongjing   

  1. State Grid Shaanxi Electric Power Co., Ltd. Economic and Technical Research Institute, Xi′an 710075,China
  • Received:2022-11-29 Revised:2023-01-03 Online:2023-04-25 Published:2023-05-06

摘要: 为精准预测输变电工程造价、控制建设阶段的成本投资、提高资金的利用率,基于陕西省内不同城市输变电工程实际发生的造价数据,利用熵权法筛选电网工程造价关键影响因素,通过BP神经网络预测模型有效地动态预测输变电工程造价,预测结果证实了模型的有效性。

关键词: BP神经网络, 输变电工程, 预测模型

Abstract: In order to accurately predict the cost of power transmission and transformation projects, control the cost investment in the construction stage, and improve the utilization rate of funds,based on the actual cost data of power transmission and transformation projects in different cities in Shaanxi province, the key influencing factors of power grid project cost are screened by using the entropy weight method, and the BP neural network forecasting model is used to predict the cost of transmission and transformation projects effectively. The forecasting results confirm the validity of the model.

Key words: BP neural network, power transmission and transformation project, prediction model

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