LM-CNN-based Automatic Cost Calculation Model for Power Transmission and Transformation Projects

doi:10.11930/j.issn.1004-9649.202103063

Abstract

Abstract: The cost calculation of power transmission and transformation project is the core part of cost control technology. The quality of the cost calculation model directly affects the efficiency and reliability of the cost management of power transmission and transformation projects. However, the existing models cannot reconcile the computational speed, accuracy and stability. Considering above-mentioned problems, firstly, a convolutional neural network model is constructed with its input and output determined according to the practical cost requirements of the power transmission and transformation projects. Then, the historical cost data are input into the network model as samples to calculate the network output. Finally, in view of the big difference between the expected output and the actual output, the Levenberg-Marquart algorithm is utilized to optimize the weight parameters of the convolutional neural network to complete the model training. Compared with the BP neural network and GD-CNN, the proposed model with higher prediction accuracy and stability combines the advantages of Levenberg-Marquart algorithm and convolutional neural network model to improve the calculation effect of power transmission and transformation project cost.

Key words: power transmission and transformation project, Levenberg-Marquart algorithm, convolutional neural network, automatic calculation model, cost control

WU Xiaolin, LUAN Ling, PAN Lianwu, LI Hailong. LM-CNN-based Automatic Cost Calculation Model for Power Transmission and Transformation Projects[J]. Electric Power, 2023, 56(2): 157-163.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202103063

https://www.electricpower.com.cn/EN/Y2023/V56/I2/157

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