摘要
传统输电线路(TTL)工程投资概算预测模型存在与实际造价偏差较大、概算管理工作效率低等问题。基于此,研究了基于主成分分析(PCA)和反向(BP)神经网络相结合的新型输电线路工程投资概算预测模型。首先,以影响输电线路工程投资的关键参数为初始输入变量,借助PCA对变量进行降维处理以简化输入数据的复杂性。其次,应用相关性剪枝算法优化BP神经网络节点数,进一步提升算法的快速性和准确性。最后,以河北省电力公司2018年01月—2020年01月输电线路工程投资概算数据为样本进行实例研究。结果表明:所设计基于PCA-BP神经网络的概算预测模型的预测准确率相比于支持向量机法(SVM)和BP神经网络法分别提升了70%和29%,具有更快的收敛速度及显著的工程应用价值。
Investment estimate prediction model in traditional transmission line(TTL)project has problems such as large deviation from the actual cost and low efficiency of estimation management.Thus,a novel investment estimate prediction model in TTL project was presented with assistance of principal component analysis and back propagation(PCA-BP)neural network.Firstly,parameters that affect the investment in TTL projects were applied as the initial input variables,which were dimensionally reduced with the help of PCA to simplify the complexity of the input data.Then,used relevance pruning algorithm to optimize number of BP neural network nodes to further improve the speed and accuracy of the proposed algorithm.Finally,an example study was carried out using data from the estimated investment budget for transmission line projects of Hebei Electric Power Company from January 2018 to January 2020 as a sample.The results show that the prediction accuracy of the designed PCA-BP neural network-based probabilistic prediction model is 70%and 29%higher than that of the traditional support vector machines(SVM)and BP neural network methods respectively,and have faster convergence and significant engineering application value.
作者
王林峰
徐楠
聂婧
谢延涛
宋妍
WANG Linfeng;XU Nan;NIE Jing;XIE Yantao;SONG Yan(Economic and Technological Research Institute,State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050000,Hebei,China)
出处
《电气传动》
2023年第9期41-48,共8页
Electric Drive
基金
国网河北省电力有限公司科技项目(B704JY200078)。
关键词
主成分分析
BP神经网络
输电线路
投资概算
principal component analysis(PCA)
back propagation(BP)neural network
transmission line
investment budget
