基于灰关联-神经网络的电力工程造价估算

Cost estimate of power line projects based on grey relational analysis and neural networks

为准确地估算和审查电力线路的工程造价,提出一种基于灰关联分析和神经网络相结合的造价估算方法。利用灰关联分析法筛选出影响工程造价的10个主要工程特征参数,以此作为神经网络输入向量,构建GRA(grey relational analysis)-ANN(artificial neural networks)造价估算模型;以某市110kV电力线路改造的工程造价资料为实验对象进行算法验证,结果显示静态投资的相对误差最大为3.72%,最小为1.85%,估算精度高;分别采用LM-BP算法和传统BP算法训练GRA-ANN网络,结果显示LM-BP法的误差下降速度更快,整体误差更低。

To accurately estimate the cost of a power line project,a method based on grey relational analysis （GRA） and neural networks （NN） is presented and studied. Grey relational analysis technologies are used to analyze the features of the transmission line project and ten main features which affect the project cost most are selected. Then, the main features are used as input neural cell of neural networks, and a model of GRA ANN is built. To verify the method, the cost data of a 110 kV power construction project are used to train and test the model. Results show the model＇s maximum relative error of static investment is 3.72% and the minimum is 1. 85%, and its accuracy is high. The LM-BP algorithm and the traditional BP algorithm are used respectively to train the GRA-ANN network, and results show the error declining rate of LM-BP algorithm is faster and the overall error is lower.