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Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method

Author

Listed:
  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yi Liang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Haichao Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Meng Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Wei-Chiang Hong

    (School of Education Intelligent Technology, Jiangsu Normal University, Xuzhou 221116, China
    Department of Information Management, Oriental Institute of Technology, New Taipei 220, Taiwan)

Abstract

Stable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) based on the variance-covariance (VC) weight determination method. Firstly, the initial weights and thresholds of BPNN are optimized by mind evolutionary computation (MEC) to prevent the BPNN from falling into local optima and speed up its convergence. Secondly, a bat algorithm (BA) is utilized to optimize the key parameters of SVM. Thirdly, the kernel function is introduced into an extreme learning machine (ELM) to improve the regression prediction accuracy of the model. Lastly, after adopting the above three modified models to predict, the variance-covariance weight determination method is applied to combine the forecasting results. Through performance verification of the model by real-world examples, the results show that the forecasting accuracy of the three individual modified models proposed in this paper has been improved, but the stability is poor, whereas the combination forecasting method proposed in this paper is not only accurate, but also stable. As a result, it can provide technical reference for the safety management of power grid.

Suggested Citation

  • Dongxiao Niu & Yi Liang & Haichao Wang & Meng Wang & Wei-Chiang Hong, 2017. "Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Wei," Energies, MDPI, vol. 10(8), pages 1-21, August.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1196-:d:108107
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    References listed on IDEAS

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    1. Stephanie E. Chang & Timothy L. McDaniels & Joey Mikawoz & Krista Peterson, 2007. "Infrastructure failure interdependencies in extreme events: power outage consequences in the 1998 Ice Storm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 41(2), pages 337-358, May.
    2. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
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    Cited by:

    1. Yuansheng Huang & Shijian Liu & Lei Yang, 2018. "Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    2. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    3. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.

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