Power engineering cost optimization algorithm based on BIM and CNN in big data environment
WANG Linfeng
ZHANG Wenjing
LIU Yun
CHEN Zhibin
WANG Ligong
Abstract：In view of the shortcomings of power engineering cost optimization in precision and dynamic aspects of big data environment,a power engineering cost optimization algorithm based on BIM and CNN was proposed.The characteristics of BIM technology were used to carry out the whole-life-cycle cost management of power engineering and realize the dynamic control of its cost.The Levenberg-Marquardt rule algorithm was used to improve the convolutional neural network(CNN),and the cost of each engineering link was predicted by the improved CNN network,so as to optimize the construction scheme for entire project.Combined with the relevant power engineering cost data,the as-proposed algorithm was tested by using Matlab.The results show that the performance of CNN network is the best when the learning rate is 0.010,and the prediction accuracy of the as-proposed algorithm is 94%,which is the most close to the real cost value.
Keywords：power engineering costBIM technologyconvolutional neural networkbig data environmentLevenberg-Marquardt rule algorithmwhole-life-cycledynamic management and controlprediction accuracy
Publication Date：2024-01-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 7-12 )