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

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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

    as
    1. Rao Rao & Xingping Zhang & Zhiping Shi & Kaiyan Luo & Zhongfu Tan & Yifan Feng, 2014. "A Systematical Framework of Schedule Risk Management for Power Grid Engineering Projects’ Sustainable Development," Sustainability, MDPI, vol. 6(10), pages 1-30, October.
    2. Jin-peng Liu & Chang-ling Li, 2017. "The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
    3. Mellal, Mohamed Arezki & Williams, Edward J., 2015. "Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem," Energy, Elsevier, vol. 93(P2), pages 1711-1718.
    4. Juntao Fan & Jin Wu & Weijing Kong & Yizhang Zhang & Mengdi Li & Yuan Zhang & Wei Meng & And Mengheng Zhang, 2017. "Predicting Bio-indicators of Aquatic Ecosystems Using the Support Vector Machine Model in the Taizi River, China," Sustainability, MDPI, vol. 9(6), pages 1-11, May.
    5. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    6. Kaijian He & Hongqian Wang & Jiangze Du & Yingchao Zou, 2016. "Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology," Energies, MDPI, vol. 9(11), pages 1-11, November.
    7. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting," Applied Energy, Elsevier, vol. 198(C), pages 203-222.
    8. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    9. Weide Li & Xuan Yang & Hao Li & Lili Su, 2017. "Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting," Energies, MDPI, vol. 10(1), pages 1-17, January.
    10. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Correction: Liang, Y., et al. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search. Energies 2016, 9 , 827," Energies, MDPI, vol. 9(12), pages 1-1, December.
    11. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search," Energies, MDPI, vol. 9(10), pages 1-17, October.
    12. Huiru Zhao & Nana Li, 2015. "Risk Evaluation of a UHV Power Transmission Construction Project Based on a Cloud Model and FCE Method for Sustainability," Sustainability, MDPI, vol. 7(3), pages 1-30, March.
    13. Nima Amjady & Oveis Abedinia, 2017. "Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine," Sustainability, MDPI, vol. 9(11), pages 1-18, November.
    14. Makhloufi, Saida & Mekhaldi, Abdelouahab & Teguar, Madjid, 2016. "Three powerful nature-inspired algorithms to optimize power flow in Algeria's Adrar power system," Energy, Elsevier, vol. 116(P1), pages 1117-1130.
    15. Jiyang Tian & Chuanzhe Li & Jia Liu & Fuliang Yu & Shuanghu Cheng & Nana Zhao & Wan Zurina Wan Jaafar, 2016. "Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test," Sustainability, MDPI, vol. 8(11), pages 1-17, October.
    16. Zhilong Wang & Chen Wang & Jie Wu, 2016. "Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms," Sustainability, MDPI, vol. 8(11), pages 1-32, November.
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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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