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

Zhang Wenzhe; Li Yangyang

doi:doi:10.11648/j.sd.20160401.11

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

Zhang Wenzhe, Li Yangyang

Published in Science Discovery (Volume 4, Issue 1)

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

Zhang Wenzhe

State Grid Chong Qing Electric Power Company, Chongqing, China
Li Yangyang

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

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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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download