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

Author

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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)

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.

Suggested Citation

  • Dongxiao Niu & Weibo Zhao & Si Li & Rongjun Chen, 2018. "Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines," Sustainability, MDPI, vol. 10(1), pages 1-11, January.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:118-:d:125663
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    References listed on IDEAS

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    1. Xiaomin Xu & Luyao Peng & Zhengsen Ji & Shipeng Zheng & Zhuxiao Tian & Shiping Geng, 2021. "Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    2. Hongwei Wang & Yuansheng Huang & Chong Gao & Yuqing Jiang, 2019. "Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network," Energies, MDPI, vol. 12(16), pages 1-21, August.

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