基于LM-CNN的输变电工程造价自动计算模型

doi:10.11930/j.issn.1004-9649.202103063

摘要/Abstract

摘要： 输变电工程造价计算作为造价管控技术的核心环节，其计算模型的好坏直接影响输变电工程造价管控效能。然而现有模型往往不能兼顾计算速度、精确性与稳定性。为解决上述问题，首先，针对输变电工程造价中的实际需求确定模型的输入与输出，构建卷积神经网络模型；然后，将历史造价数据作为样本输入网络模型，得到网络输出；最后，针对期望输出与实际输出相差较大的问题，利用列文伯格-马夸尔特算法对卷积神经网络的权重参数进行优化，完成模型训练。该模型结合列文伯格-马夸尔特算法与卷积神经网络模型的优点，相比于反向传播（BP）神经网络与梯度下降法-卷积神经网络（GD-CNN）具有更高的预测精度与稳定性，提高了输变电工程造价的计算效果。

关键词: 输变电工程, 列文伯格-马夸尔特算法, 卷积神经网络, 自动计算模型, 造价管控

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

武小琳, 栾凌, 潘连武, 李海龙. 基于LM-CNN的输变电工程造价自动计算模型[J]. 中国电力, 2023, 56(2): 157-163.

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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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202103063

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

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