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Bandwidth Forecasting for Power Communication Using Adaptive Extreme Learning Machine

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Cloud Computing and Security (ICCCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10040))

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Abstract

Bandwidth demand forecasting is the basis and foundation of the power communication network planning. In view of the traditional neural network learning speed is slow, the number of iterations is large, and the local optimal problem, an adaptive extreme learning machine model based on the theory of extreme learning machine and K nearest neighbor theory is proposed to predict the bandwidth of electric power communication. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and reduce the effect of the overfitting of networks. The proposed algorithms are validated using real data of a province in China. The results show that this method is better than the traditional neural network, auto regressive model, self organization model, and single extreme learning machine model. It can be used in electric power communication bandwidth prediction.

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Acknowledgments

This work is supported in part by, the National Natural Science Foundation of PR China (61105115), Six Talent Peaks Program of Jiangsu Province (2014-XXRJ-007), Natural Science Foundation of Jiangsu Province (BK20161533), Perspective Research Foundation of Production Study and Research Alliance of Jiangsu Province (BY2015007-01), and Laboratory open project of Nuist.

Author information

Authors and Affiliations

  1. State Grid Henan Economics Research Institute, Zheng Zhou, 450052, China

    Zheng Zheng & Li Di

  2. State Grid Henan Electric Power Company, Zheng Zhou, 450052, China

    Song Wang

  3. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Min Xia, Kai Hu & Ruidong Zhang

Authors
  1. Zheng Zheng
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  2. Li Di
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  3. Song Wang
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  4. Min Xia
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  5. Kai Hu
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  6. Ruidong Zhang
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Corresponding author

Correspondence to Min Xia .

Editor information

Editors and Affiliations

  1. University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Michigan State University , EAST LANSING, Michigan, USA

    Alex Liu

  3. National Dong Hwa University , Shoufeng, Taiwan

    Han-Chieh Chao

  4. Purdue University , West Lafayette, Indiana, USA

    Elisa Bertino

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Zheng, Z., Di, L., Wang, S., Xia, M., Hu, K., Zhang, R. (2016). Bandwidth Forecasting for Power Communication Using Adaptive Extreme Learning Machine. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-48674-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48673-4

  • Online ISBN: 978-3-319-48674-1

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