Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model

Zhonghua He; Tao Zhang; Fuqiang Li; Yuou Hu; Nana Li

doi:doi:10.11648/j.se.20180601.12

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Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model

Zhonghua He, Tao Zhang, Fuqiang Li, Yuou Hu, Nana Li

Published in Software Engineering (Volume 6, Issue 1)

Received: 13 December 2017 Accepted: 6 January 2018 Published: 19 January 2018

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Abstract

The accuracy of the power demand forecast will directly affect the planning, safety and stability of the power system. The power demand is greatly affected by the factors related to economic development. In addition, special events will also have impacts on the electricity consumption of industries, service industries and residents. In this paper, gray relational analysis and support vector machine intelligent algorithm are used to build a rolling power demand forecasting method based on the development of power economy. By considering the impact of special periods on power demand, this paper forecasts the power demand for special period in Beijing, Tianjin and Tangshan. Finally, the analysis shows that the electricity demand in Beijing, Tianjin and Tangshan in 2017 is 3,337 billion kwh.

Published in	Software Engineering (Volume 6, Issue 1)
DOI	10.11648/j.se.20180601.12
Page(s)	7-11
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), 2018. Published by Science Publishing Group

Keywords

Beijing-Tianjin-Tang Region, Support Vector Machines, Special Period, Power Demand Forecasting

References

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[4]	Boroojeni K G, Amini M H, Bahrami S, et al. A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon [J]. Electric Power Systems Research, 2017, 142:58-73.
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[6]	Wang Q, Wang Y L, Zhang L Z. An Approach to Allocate Impersonal Weights of Factors Influencing Electric Power Demand Forecasting [J]. Power System Technology, 2008, 32 (5):82-86.
[7]	Tong S, Koller D. Support vector machine active learning with applications to text classification [M]. JMLR. org, 2002.
[8]	Furey T S, Cristianini N, Duffy N, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. [J]. Bioinformatics, 2000, 16 (10):906.
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[12]	Cao L J, Tay F H. Support vector machine with adaptive parameters in financial time series forecasting. [J]. IEEE Transactions on Neural Networks, 2003, 14 (6):1506-18.

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APA Style

Zhonghua He, Tao Zhang, Fuqiang Li, Yuou Hu, Nana Li. (2018). Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model. Software Engineering, 6(1), 7-11. https://doi.org/10.11648/j.se.20180601.12

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ACS Style

Zhonghua He; Tao Zhang; Fuqiang Li; Yuou Hu; Nana Li. Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model. Softw. Eng. 2018, 6(1), 7-11. doi: 10.11648/j.se.20180601.12

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AMA Style

Zhonghua He, Tao Zhang, Fuqiang Li, Yuou Hu, Nana Li. Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model. Softw Eng. 2018;6(1):7-11. doi: 10.11648/j.se.20180601.12

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@article{10.11648/j.se.20180601.12,
  author = {Zhonghua He and Tao Zhang and Fuqiang Li and Yuou Hu and Nana Li},
  title = {Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model},
  journal = {Software Engineering},
  volume = {6},
  number = {1},
  pages = {7-11},
  doi = {10.11648/j.se.20180601.12},
  url = {https://doi.org/10.11648/j.se.20180601.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20180601.12},
  abstract = {The accuracy of the power demand forecast will directly affect the planning, safety and stability of the power system. The power demand is greatly affected by the factors related to economic development. In addition, special events will also have impacts on the electricity consumption of industries, service industries and residents. In this paper, gray relational analysis and support vector machine intelligent algorithm are used to build a rolling power demand forecasting method based on the development of power economy. By considering the impact of special periods on power demand, this paper forecasts the power demand for special period in Beijing, Tianjin and Tangshan. Finally, the analysis shows that the electricity demand in Beijing, Tianjin and Tangshan in 2017 is 3,337 billion kwh.},
 year = {2018}
}

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TY  - JOUR
T1  - Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model
AU  - Zhonghua He
AU  - Tao Zhang
AU  - Fuqiang Li
AU  - Yuou Hu
AU  - Nana Li
Y1  - 2018/01/19
PY  - 2018
N1  - https://doi.org/10.11648/j.se.20180601.12
DO  - 10.11648/j.se.20180601.12
T2  - Software Engineering
JF  - Software Engineering
JO  - Software Engineering
SP  - 7
EP  - 11
PB  - Science Publishing Group
SN  - 2376-8037
UR  - https://doi.org/10.11648/j.se.20180601.12
AB  - The accuracy of the power demand forecast will directly affect the planning, safety and stability of the power system. The power demand is greatly affected by the factors related to economic development. In addition, special events will also have impacts on the electricity consumption of industries, service industries and residents. In this paper, gray relational analysis and support vector machine intelligent algorithm are used to build a rolling power demand forecasting method based on the development of power economy. By considering the impact of special periods on power demand, this paper forecasts the power demand for special period in Beijing, Tianjin and Tangshan. Finally, the analysis shows that the electricity demand in Beijing, Tianjin and Tangshan in 2017 is 3,337 billion kwh.
VL  - 6
IS  - 1
ER  -

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Author Information

Zhonghua He

North China Branch of State Grid Corporation of China, Beijing, China
Tao Zhang

North China Branch of State Grid Corporation of China, Beijing, China
Fuqiang Li

North China Branch of State Grid Corporation of China, Beijing, China
Yuou Hu

North China Branch of State Grid Corporation of China, Beijing, China
Nana Li

State Grid Energy Research Institute Limited Company, Beijing, China

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Plain Text BibTeX RIS

APA Style

Zhonghua He, Tao Zhang, Fuqiang Li, Yuou Hu, Nana Li. (2018). Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model. Software Engineering, 6(1), 7-11. https://doi.org/10.11648/j.se.20180601.12

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ACS Style

Zhonghua He; Tao Zhang; Fuqiang Li; Yuou Hu; Nana Li. Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model. Softw. Eng. 2018, 6(1), 7-11. doi: 10.11648/j.se.20180601.12

Copy | Download

AMA Style

Zhonghua He, Tao Zhang, Fuqiang Li, Yuou Hu, Nana Li. Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model. Softw Eng. 2018;6(1):7-11. doi: 10.11648/j.se.20180601.12

Copy | Download

@article{10.11648/j.se.20180601.12,
  author = {Zhonghua He and Tao Zhang and Fuqiang Li and Yuou Hu and Nana Li},
  title = {Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model},
  journal = {Software Engineering},
  volume = {6},
  number = {1},
  pages = {7-11},
  doi = {10.11648/j.se.20180601.12},
  url = {https://doi.org/10.11648/j.se.20180601.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20180601.12},
  abstract = {The accuracy of the power demand forecast will directly affect the planning, safety and stability of the power system. The power demand is greatly affected by the factors related to economic development. In addition, special events will also have impacts on the electricity consumption of industries, service industries and residents. In this paper, gray relational analysis and support vector machine intelligent algorithm are used to build a rolling power demand forecasting method based on the development of power economy. By considering the impact of special periods on power demand, this paper forecasts the power demand for special period in Beijing, Tianjin and Tangshan. Finally, the analysis shows that the electricity demand in Beijing, Tianjin and Tangshan in 2017 is 3,337 billion kwh.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model
AU  - Zhonghua He
AU  - Tao Zhang
AU  - Fuqiang Li
AU  - Yuou Hu
AU  - Nana Li
Y1  - 2018/01/19
PY  - 2018
N1  - https://doi.org/10.11648/j.se.20180601.12
DO  - 10.11648/j.se.20180601.12
T2  - Software Engineering
JF  - Software Engineering
JO  - Software Engineering
SP  - 7
EP  - 11
PB  - Science Publishing Group
SN  - 2376-8037
UR  - https://doi.org/10.11648/j.se.20180601.12
AB  - The accuracy of the power demand forecast will directly affect the planning, safety and stability of the power system. The power demand is greatly affected by the factors related to economic development. In addition, special events will also have impacts on the electricity consumption of industries, service industries and residents. In this paper, gray relational analysis and support vector machine intelligent algorithm are used to build a rolling power demand forecasting method based on the development of power economy. By considering the impact of special periods on power demand, this paper forecasts the power demand for special period in Beijing, Tianjin and Tangshan. Finally, the analysis shows that the electricity demand in Beijing, Tianjin and Tangshan in 2017 is 3,337 billion kwh.
VL  - 6
IS  - 1
ER  -

Copy | Download