Substation project cost prediction method based on improved particle swarm optimization-least squares support vector regression algorithm
WANG Linfeng
LIU Yun
QI Yanxun
ZHOU Bo
LI Jie
Abstract：[Objective]The prediction of substation project cost in power grid construction projects has always been an important issue influencing project cost management.However,the currently commonly used substation cost prediction methods have problems such as insufficient prediction accuracy and low computational efficiency,which restricts the application of prediction models in actual projects.To improve the accuracy and computational efficiency of prediction,a substation project cost prediction method was proposed by combining the improved particle swarm optimization(IPSO)algorithm and least squares support vector regression(LSSVR)algorithm.[Methods]First,considering the differences in equipment,technology,and operation and maintenance between conventional substations and intelligent substations,the characteristics of these two types of substations were analyzed,and targeted preprocessing was performed on the relevant data to remove the noisy data,fill in the missing values,and convert valid information into feature vectors to be used as inputs of the LSSVR model.Next,to avoid the problem that the traditional particle swarm optimization(PSO)algorithm was prone to fall into the locally optimal solution,a hybrid adjustment strategy was introduced to optimize the inertia weights and learning factors of the PSO algorithm,which made the optimization process more stable and had a strong global search capability.With the help of this strategy,IPSO algorithm could achieve a better balance between global and local search.Finally,the IPSO algorithm was used to optimize the parameters of the LSSVR model,and a substation project cost prediction model was built.[Results]It is found from comparison with other prediction models that the proposed IPSO-LSSVR algorithm has significant advantages in prediction accuracy.Specifically,the prediction error of the model is significantly lower than those of other methods,and the deviation can be controlled within 5％.The IPSO algorithm can effectively avoid falling into local optima,which ensures that the LSSVR model can provide accurate prediction results in various situations.[Conclusion]The substation project cost prediction method based on IPSO optimized LSSVR overcomes the shortcomings of traditional prediction methods in terms of prediction accuracy and computational efficiency.In practical application,this method can provide a more accurate prediction basis for the cost management of power grid construction projects and thereby help the rational formulation of project budgets and the effective allocation of resources.
Keywords：substationproject costcost predictionparticle swarm algorithmleast squares support vector regressionprediction accuracycomputational efficiencyhybrid adjustment strategy
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 168-175 )