Software Engineering

| Peer-Reviewed |

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

Received: 13 December 2017    Accepted: 06 January 2018    Published: 19 January 2018
Views:       Downloads:

Share This Article

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.

DOI 10.11648/j.se.20180601.12
Published in Software Engineering (Volume 6, Issue 1, March 2018)
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), 2024. Published by Science Publishing Group

Keywords

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

References
[1] Dong G L, Lee B W, Chang S H. Genetic programming model for long-term forecasting of electric power demand [J]. Electric Power Systems Research, 1997, 40 (1):17-22.
[2] Gupta P C. A Stochastic Approach to Peak Power-Demand Forecasting in Electric Utility Systems [J]. IEEE Transactions on Power Apparatus & Systems, 1971, pas-90 (2):824-832.
[3] Hsu C C, Chen C Y. Applications of improved grey prediction model for power demand forecasting [J]. Energy Conversion & Management, 2003, 44 (14):2241-2249.
[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.
[5] Kabir G, Sumi R S. Integrating fuzzy Delphi method with artificial neural network for demand forecasting of power engineering company [J]. Management Science Letters, 2012, 2 (5):1491-1504.
[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.
[9] Hua S, Sun Z. Support vector machine approach for protein subcellular localization prediction. [J]. Bioinformatics, 2001, 17 (8):721-8.
[10] Suykens J A K, Gestel T V, Brabanter J D, et al. Least Square Support Vector Machine [J]. Euphytica, 2002, 2 (2):1599-1604 vol.2.
[11] Mukherjee S, Osuna E, Girosi A F. Nonlinear Prediction of Chaotic Time Series using a Support Vector Machine [J]. 2008:511-520.
[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.
Author Information
  • North China Branch of State Grid Corporation of China, Beijing, China

  • North China Branch of State Grid Corporation of China, Beijing, China

  • North China Branch of State Grid Corporation of China, Beijing, China

  • North China Branch of State Grid Corporation of China, Beijing, China

  • State Grid Energy Research Institute Limited Company, Beijing, China

Cite This Article
  • 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

    Copy | Download

    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://download.sciencepg.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

  • Sections