TY - JOUR
TI - Cost prediction on fabricated substation considering support vector machine via optimized quantum particle swarm optimization
AU - Xiong Yi
AU - Ming Yue
AU - Liao Xiaohong
AU - Xiong Chuanyu
AU - Wen Wu
AU - Xiong Zhiwei
AU - Li Lie
AU - Sun Lipin
AU - Zhou Qiupeng
AU - Zou Yuxin
AU - Guo Ting
AU - Ma Li
AU - Liao Shuang
JN - Thermal Science
PY - 2020
VL - 24
IS - 5
SP - 2773
EP - 2780
PT - Article
AB - 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.