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Project Cost Prediction of Overhead Line Based on Big Data Analysis of Power Grid Engineering

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Cyber Security Intelligence and Analytics (CSIA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1342))

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Abstract

Many factors affect the cost level of overhead lines, such as different macroeconomics, natural conditions, technical conditions and external construction environment, and overhead line engineering cost prediction is an important part of project management and control. In order to obtain more accurate prediction results, this paper is based on gray correlation Analyze and screen the factors that affect the cost of overhead line projects, then use the particle swarm improved support vector machine algorithm to establish a smart transmission line project cost prediction model, and use the actual samples of power grid engineering big data to verify the effectiveness of the proposed method. Reasonable determination of power grid investment and decision-making provides a basis.

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Authors and Affiliations

  1. School of Economics and Management, North China Electric Power University, Beijing, 102206, China

    Shiping Geng, Zhuxiao Tian, Zhengsen Ji, Dongxiao Niu & Xiaopeng Guo

Authors
  1. Shiping Geng
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  2. Zhuxiao Tian
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  3. Zhengsen Ji
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  4. Dongxiao Niu
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  5. Xiaopeng Guo
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Corresponding author

Correspondence to Shiping Geng .

Editor information

Editors and Affiliations

  1. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

  2. Department Software Engineering and Game Development, Kennesaw State University, Marietta, GA, USA

    Reza M. Parizi

  3. School of Engineering and Sciences, Tecnologico de Monterrey, Puebla, Mexico

    Octavio Loyola-González

  4. Assistant Professor in Practice, University of Texas at San Antonio, Boerne, TX, USA

    Xiaolu Zhang

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© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Geng, S., Tian, Z., Ji, Z., Niu, D., Guo, X. (2021). Project Cost Prediction of Overhead Line Based on Big Data Analysis of Power Grid Engineering. In: Xu, Z., Parizi, R.M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing, vol 1342. Springer, Cham. https://doi.org/10.1007/978-3-030-70042-3_80

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