Open AccessLife Cycle Cost Prediction of Substation Based on Advanced PSO and Least Squares Support Vector Machine
Author(s) -
Yi Xiong,
Congcong Xiong,
Wen Wu,
Xiong Zhang,
Lie Li,
Xiaofeng Liao,
Li Sun,
Qiupeng Zhou,
Yuxin Zou,
Shuang Liu,
Yue Ming,
Tao Guo,
Li Ma
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/461/1/012083
Subject(s) - support vector machine , particle swarm optimization , artificial neural network , engineering , least squares support vector machine , computer science , reliability engineering , machine learning , artificial intelligence
The rapid prediction of the full life cycle cost of substation has guiding significance for the construction of substation. In this paper, a substation full life cycle cost prediction model based on advanced particle swarm optimization (advanced PSO, APSO) least squares support vector machine is established. The relevant characteristic index of the substation life cycle is used as the input of the model, and the output is the substation full life cycle cost. The simulation results are compared with the prediction results of APSO optimized LS-SVM, traditional LS-SVM, BP neural network four prediction models and related performance indicators. The simulation results show that the APSO optimized LS-SVM model has better prediction accuracy, and can predict and evaluate the life cycle cost quickly and accurately during substation design and construction, and improve the economics of substation construction.