Life Cycle Cost Prediction of Substation Based on Advanced PSO and Least Squares Support Vector Machine
Xiong Yi, Xiong Chuanyu, Wen Wu, Xiong Zhiwei, Lie Li, Xiaohong Liao, Sun Lipin, Qiupeng Zhou, Zou Yuxin, Liao Shuang, Ming Yue, Ting Biao Guo, Ma Li · IOP Conference Series Earth and Environmental Science · 2020
Abstract 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.