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