THERMAL SCIENCE

International Scientific Journal

Yi Xiong

,

Yue Ming

,

Xiaohong Liao

,

Chuanyu Xiong

,

Wu Wen

,

Zhiwei Xiong

,

Lie Li

,

Lipin Sun

,

Qiupeng Zhou

,

Yuxin Zou

,

Ting Guo

,

Li Ma

,

Shuang Liao

COST PREDICTION ON FABRICATED SUBSTATION CONSIDERING SUPPORT VECTOR MACHINE VIA OPTIMIZED QUANTUM PARTICLE SWARM OPTIMIZATION

ABSTRACT
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.
KEYWORDS
life cycle cost, quantum PSO, LS-SVM, characteristic parameters, fitness function
PAPER SUBMITTED: 2019-08-29
PAPER REVISED: 2019-11-20
PAPER ACCEPTED: 2019-12-06
PUBLISHED ONLINE: 2020-01-19
DOI REFERENCE: https://doi.org/10.2298/TSCI190829010X
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2020, VOLUME 24, ISSUE Issue 5, PAGES [2773 - 2780]
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Cost prediction on fabricated substation considering support vector machine via optimized quantum particle swarm optimization

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence