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Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting

Received: 7 April 2016     Published: 8 April 2016
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Abstract

The paper improves the prediction accuracy of the monthly electricity consumption of power system, a hybrid prediction model is put forward aiming at the problems existing in the traditional grey prediction method, which is based on the combined optimization model of particle swarm and K nearest value. Grey prediction equation is solved by particle swarm optimization algorithm, which is a good solution to the problem of the choice of parameters of gray prediction equation, with strong global optimization ability; A combinatorial optimization algorithm of the K- nearest value and particle swarm was proposed, which solves the problem of prediction error caused by large fluctuations of raw data, and improves the accuracy of prediction results. Through the prediction of monthly electricity consumption in the past years, the results show that the combination prediction method proposed in this paper can effectively predict the monthly electricity consumption, and is practical.

Published in Science Discovery (Volume 4, Issue 1)
DOI 10.11648/j.sd.20160401.11
Page(s) 1-5
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Monthly Prediction of Electricity Consumption, Particle Swarm, K Nearest Neighbor, Combination Optimization

References
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  • APA Style

    Zhang Wenzhe, Li Yangyang. (2016). Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting. Science Discovery, 4(1), 1-5. https://doi.org/10.11648/j.sd.20160401.11

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    ACS Style

    Zhang Wenzhe; Li Yangyang. Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting. Sci. Discov. 2016, 4(1), 1-5. doi: 10.11648/j.sd.20160401.11

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    AMA Style

    Zhang Wenzhe, Li Yangyang. Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting. Sci Discov. 2016;4(1):1-5. doi: 10.11648/j.sd.20160401.11

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  • @article{10.11648/j.sd.20160401.11,
      author = {Zhang Wenzhe and Li Yangyang},
      title = {Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting},
      journal = {Science Discovery},
      volume = {4},
      number = {1},
      pages = {1-5},
      doi = {10.11648/j.sd.20160401.11},
      url = {https://doi.org/10.11648/j.sd.20160401.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160401.11},
      abstract = {The paper improves the prediction accuracy of the monthly electricity consumption of power system, a hybrid prediction model is put forward aiming at the problems existing in the traditional grey prediction method, which is based on the combined optimization model of particle swarm and K nearest value. Grey prediction equation is solved by particle swarm optimization algorithm, which is a good solution to the problem of the choice of parameters of gray prediction equation, with strong global optimization ability; A combinatorial optimization algorithm of the K- nearest value and particle swarm was proposed, which solves the problem of prediction error caused by large fluctuations of raw data, and improves the accuracy of prediction results. Through the prediction of monthly electricity consumption in the past years, the results show that the combination prediction method proposed in this paper can effectively predict the monthly electricity consumption, and is practical.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting
    AU  - Zhang Wenzhe
    AU  - Li Yangyang
    Y1  - 2016/04/08
    PY  - 2016
    N1  - https://doi.org/10.11648/j.sd.20160401.11
    DO  - 10.11648/j.sd.20160401.11
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 1
    EP  - 5
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20160401.11
    AB  - The paper improves the prediction accuracy of the monthly electricity consumption of power system, a hybrid prediction model is put forward aiming at the problems existing in the traditional grey prediction method, which is based on the combined optimization model of particle swarm and K nearest value. Grey prediction equation is solved by particle swarm optimization algorithm, which is a good solution to the problem of the choice of parameters of gray prediction equation, with strong global optimization ability; A combinatorial optimization algorithm of the K- nearest value and particle swarm was proposed, which solves the problem of prediction error caused by large fluctuations of raw data, and improves the accuracy of prediction results. Through the prediction of monthly electricity consumption in the past years, the results show that the combination prediction method proposed in this paper can effectively predict the monthly electricity consumption, and is practical.
    VL  - 4
    IS  - 1
    ER  - 

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Author Information
  • State Grid Chong Qing Electric Power Company, Chongqing, China

  • Central China Science and Technology Development Co., Ltd., Wuhan, China

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