Substation Life Cycle Cost Prediction Model of the Least Squares Support Vector Machine Optimized by Genetic Algorithm

doi:10.11930/j.issn.10.11930.2015.11.142

Abstract

Abstract: Because of large number of equipment and investment during substation construction, building a reasonable life cycle cost (LCC) prediction model is an important means of improving grid assets management efficiency. The methods to ensure convergence capability and improve prediction accuracy are difficulties in current study. A substation LCC prediction model based on least squares support vector machine optimized by GA is proposed. The method use some representative indexes as predictive model input vectors and the total substation LLC cost as the output vector. The results from traditional LS-SVM prediction model, BP neural network prediction model and GA optimized LS-SVM prediction model are compared on selected examples. The comparison verifies superior performance of proposed model. The proposed method can be used during new substation planning and construction to predict station LLC quickly and efficiently.

Key words: substation, life cycle cost, genetic algorithm, support vector machine

CLC Number:

TM711

QIAO Guohua, GUO Luyao, Wu Yidi, LI Jing, JIA Zhaoyang, HAO Feng, ZHAN Xiangling, WANG Yayun. Substation Life Cycle Cost Prediction Model of the Least Squares Support Vector Machine Optimized by Genetic Algorithm[J]. journal1, 2015, 48(11): 142-148.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.10.11930.2015.11.142

https://www.electricpower.com.cn/EN/Y2015/V48/I11/142

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