Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

Xiaomin Xu, Luyao Peng, Zhengsen Ji, Shipeng Zheng, Zhuxiao Tian and Shiping Geng
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Xiaomin Xu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Luyao Peng: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Zhengsen Ji: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Shipeng Zheng: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Zhuxiao Tian: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Shiping Geng: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2021, vol. 13, issue 24, 1-17

Abstract: The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research.

Keywords: project cost; data space; intelligent prediction; sparrow search algorithm; BP neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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