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User Behavior Research Based on Big Data

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Book cover Machine Learning and Cybernetics (ICMLC 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 481))

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Abstract

In this paper, the enterprise user behavior had been studied based on big data. By combining cloudy computing and k-means clustering algorithm, we proposed the parallel k-mean clustering. The feature were chosen as follows: Power consumption rate in the peak load time; the load rate and the power consumption rate in the valley time and so on. The feature weight can be calculated with entropy weight method. The experimental data came from the intelligent industrial park of Gansu province. The enterprise users are classified into two classes, the different type enterprise has their electricity law. In the future, enterprise can optimize their working time, lower the electricity cost in the same power consumption. This provides strong support for the demand of side response of power grid.

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Author information

Authors and Affiliations

  1. State Grid Information and Telecommunication Co. Ltd., Beijing, 100761, China

    Suxiang Zhang

  2. Department of Foreign Language of Hebei University, Baoding, 071000, China

    Suxian Zhang

Authors
  1. Suxiang Zhang
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  2. Suxian Zhang
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Corresponding author

Correspondence to Suxian Zhang .

Editor information

Editors and Affiliations

  1. Hebei University, Baoding, China

    Xizhao Wang

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. South China University of Technology, Guangzhou, China

    Patrick Chan

  4. Hebei University, Baoding, China

    Qiang He

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© 2014 Springer-Verlag Berlin Heidelberg

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Zhang, S., Zhang, S. (2014). User Behavior Research Based on Big Data. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_17

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  • DOI: https://doi.org/10.1007/978-3-662-45652-1_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45651-4

  • Online ISBN: 978-3-662-45652-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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