基于BP神经网络优化算法的输变电工程造价预测模型  

Cost prediction model based on BP neural network optimization algorithm for power transmission and transformation projects

作　　者：张恒武 吴小忠 沈晓隶 伍家耀 ZHANG Heng-wu;WU Xiao-zhong;SHEN Xiao-li;WU Jia-yao(Construction Department,State Grid Hunan Electric Power Co.Ltd.,Changsha 410000,China;Technical Economics Department,Hunan Jingyan Electric Power Design Co.Ltd.,Changsha 410000,China)

机构地区：[1]国网湖南省电力有限公司建设部,长沙410000 [2]湖南经研电力设计有限公司技术经济部,长沙410000

出　　处：《沈阳工业大学学报》2023年第4期381-386,共6页Journal of Shenyang University of Technology

基　　金：湖南省科技计划项目(S2019RCDT2B0484).

摘　　要：针对现有的输变电工程造价预测方法在复杂工况下精度低的问题,提出了一种基于BP神经网络优化算法的输变电工程造价预测模型.利用因子分析方法确定输变电工程造价数据预测的输入指标,并在传统BP神经网络模型的基础上,引入思维进化算法对BP神经网络中的权值和阈值进行优化.利用构建的预测模型预测某省级电网公司2016年度的输变电工程造价.结果表明,预测误差低于10%,平均误差低于5%.与传统的BP神经网络相比,所提预测模型具有更高的预测精度,可以较好地应用于输变电工程造价估算.Aiming at the low accuracy of existing cost prediction methods for power transmission and transformation projects under complex working conditions,a cost prediction model based on BP neural network optimization algorithm for the power transmission and transformation projects was proposed.The input indexes for the data prediction of power transmission and transformation were determined by a factor analysis method.According to the traditional BP neural network model,the weight and threshold values of BP neural network were optimized by introducing a mind evolution algorithm.Using the as-proposed prediction model,the cost of power transmission and transformation projects in 2016 for a provincial power grid company was predicted.The results show that the prediction error is less than 10%,and the average error is less than 5%.Compared with the traditional BP neural network,the as-proposed prediction model has high prediction accuracy,and can be preferably applied to the cost estimation of power transmission and transformation projects.

关 键 词：输电工程 变电工程 造价预测 BP神经网络 优化算法 因子分析 思维进化算法 预测精度 输入指标 

分 类 号：TM73[电气工程—电力系统及自动化]