Cost estimation technology for power transmission and transformation projects based on radial basis function neural network and significant cost theory
LIU Hongzhi
JIN Shudong
TAO Xisheng
KONG Chao
LI Yan
Abstract：[Objective]With the increasing importance of power transmission and transformation projects in distribution networks,traditional cost estimation methods face challenges such as large errors and excessive time consumption,making them inadequate for modern engineering management.To enhance the execution efficiency and estimation accuracy of power transmission and transformation projects,this study proposed a novel cost estimation method based on radial basis function neural network(RBFNN)and significance cost theory.This approach aims to address the limitations of traditional methods in complex cost estimation scenarios while enhancing the robustness and adaptability of the model.[Methods]This study applied significance cost theory to screen historical project data and identify the main factors affecting cost estimation for power transmission and transformation projects.These factors were used as input features for the neural network.The method introduced radial basis functions(RBFs)to restructure the traditional artificial neural network(ANN)architecture,creating a cost estimation model specifically for power transmission and substation projects.The model processed input data using Gaussian functions,initialized the hidden layer centers with the K-means clustering algorithm,and used least squares and gradient descent methods to train the output and hidden layers.To validate the model's effectiveness,100 sets of data from power transmission and transformation projects were used to compare the cumulative absolute error rate and average execution time of traditional methods(unit cost method and index estimation method)with the proposed model.Moreover,SHAP value analysis was employed to quantify the impact of key factors on estimation error rates.[Results]Simulation results demonstrate that the RBFNN-based cost estimation method outperforms traditional methods in both cumulative absolute error rate and execution time.When the test sample size increases to 20,the cumulative error rate for the unit cost method reaches 440％,while the index estimation method reaches 180％,and the proposed model maintains an error rate below 110％.In terms of execution time,traditional methods require an average of 5 s,while the proposed model reduces the time to just 0.5 s.In addition,SHAP value analysis reveals that factors such as wire cross-sectional area,steel pipe poles,and the number of circuits have the greatest influence on estimation error rates,with their SHAP values significantly higher than those of other factors.This finding provides critical insights for model optimization and cost control.[Conclusions]The cost estimation method proposed in this paper,based on RBFNN and significance cost theory,effectively improves the accuracy and efficiency of cost estimation for power transmission and transformation projects.Although the method still exhibits some errors in complex construction environments,it outperforms traditional methods in overall performance,making it highly practical with strong potential for widespread application.Future research will focus on integrating regression analysis,support vector machine(SVM),and other machine learning algorithms to further optimize model precision and better handle the complexity and variability of power transmission and transformation projects.
Keywords：radial basis function neural networkpower transmission and transformation projectcost estimationsignificant cost theoryrobustnessfeature screeningadaptabilityerror rate
Publication Date：2025-11-25
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：7( 744-750 )