Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines

Dongxiao Niu, Weibo Zhao, Si Li and Rongjun Chen
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Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Weibo Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Si Li: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Rongjun Chen: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2018, vol. 10, issue 1, 1-11

Abstract: Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD) can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS) algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM) model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy.

Keywords: cost prediction of substation project; Ensemble Empirical Mode Decomposition; Cuckoo Search; Support Vector Machines (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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