Power engineering cost prediction based on improved SVM
LIU Yun
LI Weijia
ZHAO Zihao
DONG Zhenliang
CHEN Zhibin
Abstract：Aiming at the problems of lower solving speed of support vector machine(SVM)and unsatisfactory performance in predicting power engineering cost,a power engineering cost prediction model based on improved SVM was proposed.This model comprehensively considered the constituent elements of power engineering costs and the normalization of parameters.The least squares estimation was utilized to improve the SVM model,and genetic algorithm(GA)was used to solve for the optimal parameter values of LSSVM.The prediction for power engineering cost was conducted by the optimized GA-LSSVM model.The simulation experiment results based on MATLAB simulation platform show that the predicted engineering cost value is relatively close to the actual value.The normalized mean square error and average absolute percentage error are 183400 yuan and 3.58%,respectively,and the prediction time is 256 ms.The overall performance is better than other comparison models.
Keywords：power engineeringcost predictionsupport vector machineleast squares estimationgenetic algorithmGA-LSSVM modelnormalization processingerror analysis
Publication Date：2024-07-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 367-372 )