Cost prediction on fabricated substation considering support vector machine via optimized quantum particle swarm optimization

Thermal Science

Li Lie¹  Wen Wu¹  Xiong Zhiwei¹  Liao Shuang²  Sun Lipin²  Ma Li²  Zou Yuxin²  Ming Yue²  Xiong Yi²  Liao Xiaohong²  Xiong Chuanyu²  Guo Ting²  Zhou Qiupeng² 

[1] School of Electrical Engineering and Automation, Wuhan University, Wuhan, China;State Grid Hubei Electric Power Company Economic Technology Research Institute, Wuhan, China;

关键词: life cycle cost;    quantum pso;    ls-svm;    characteristic parameters;    fitness function;   

DOI  :  10.2298/TSCI190829010X

来源: DOAJ

【 摘 要 】

At present, the prediction of the life cycle cost of fabricated substation is of great significance for the construction of fabricated substation. An enhanced prediction model based on quantum particle swarm optimization (QPSO) via least squares support vector machine is established. The relevant characteristic index of the life cycle of the fabricated substation is used as the input of the model, and the output is the life cycle cost. The simulation results are compared with the prediction results of QPSO optimized least squares support vector machine (LS-SVM), PSO optimized LS-SVM, traditional LS-SVM, and backpropagation neural network, which shows that the QPSO optimized LS-SVM model has better prediction accuracy, can predict and evaluate the life cycle cost more quickly, and can improve the benefits of fabricated substation construction.

【 授权许可】

Unknown