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Electricity Demand Prediction of Beijing during the 13th Five-year

Received: 14 May 2015     Accepted: 14 June 2015     Published: 3 August 2015
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Abstract

With the emergence of a “new economic norm” and the development of “economic integration in Beijing, Tianjin and Hebei”, electricity demand situation in Beijing will change significantly in the future. To guide the planning and construction of power grid in Beijing, it is indispensable to predict electricity demand during the 13th Five-year. Since the factors and affecting mechanisms for electricity demand are different in different sectors, the total electricity consumption in this paper is divided into five parts: the first industry, industry, construction industry, the tertiary industry and resident sectors. The exponential smoothing method and co-integration theory are introduced to establish the forecasting model of electricity demand in different sectors. Therefore, based on the forecasting model and scenario analysis, the analysis results show that the total electricity consumption will grow at an annual rate of 4.9%-6.0% during 13th Five-Year-Plan period, and the consumption would reach more than 0.1397×1012 kWh in 2020.

Published in International Journal of Energy and Power Engineering (Volume 4, Issue 4-1)

This article belongs to the Special Issue Current Energy Issues in China

DOI 10.11648/j.ijepe.s.2015040401.13
Page(s) 12-16
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), 2015. Published by Science Publishing Group

Keywords

Electricity demand, forecasting, subsectors, Exponential smoothing method, co-integration theory

References
[1] Elakrmi F, Shikhah N A. Electricity Demand Forecasting[J]. Business Intelligence in Economic Forecasting: Technologies and Techniques: Technologies and Techniques, 2010: 296.
[2] Yao A W L, Chi S C, Chen J H. An improved grey-based approach for electricity demand forecasting[J]. Electric Power Systems Research, 2003, 67(3): 217-224.
[3] Boqiang L. Structural changes, efficiency improvement and electricity demand forecasting[J]. Economic Research Journal, 2003, 5: 7-9.
[4] Taylor J W, Majithia S. Using combined forecasts with changing weights for electricity demand profiling[J]. Journal of the Operational Research Society, 2000: 72-82.
[5] Gardner E S. Exponential smoothing: The state of the art—Part II[J]. International journal of forecasting, 2006, 22(4): 637-666.
[6] Christiaanse W R. Short-term load forecasting using general exponential smoothing[J]. Power Apparatus and Systems, IEEE Transactions on, 1971 (2): 900-911.
[7] Xiaoping H, Xiying L, Yanping L. China's Electricity Demand Forecast under Urbanization Process [J][J]. Economic Research Journal, 2009, 1: 118-130.
[8] Yoo S H. The causal relationship between electricity consumption and economic growth in the ASEAN countries[J]. Energy Policy, 2006, 34(18): 3573-3582.
[9] Xie P, Tan Z, Hou J, et al. Analysis on dynamic relationship between urbanization and electricity consumption level in China[J]. Power System Technology, 2009, 33(14): 72-77.
[10] Ang B W. Decomposition methodology in industrial energy demand analysis[J]. Energy, 1995, 20(11): 1081-1095.
[11] Ozturk H K, Ceylan H. Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study[J]. International Journal of Energy Research, 2005, 29(9): 829-840.
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[13] Narayan P K, Smyth R, Prasad A. Electricity consumption in G7 countries: A panel cointegration analysis of residential demand elasticities[J]. Energy policy, 2007, 35(9): 4485-4494.
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  • APA Style

    Na-na Li, Hui-ru Zhao, Ming-rui Zhao. (2015). Electricity Demand Prediction of Beijing during the 13th Five-year. International Journal of Energy and Power Engineering, 4(4-1), 12-16. https://doi.org/10.11648/j.ijepe.s.2015040401.13

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    ACS Style

    Na-na Li; Hui-ru Zhao; Ming-rui Zhao. Electricity Demand Prediction of Beijing during the 13th Five-year. Int. J. Energy Power Eng. 2015, 4(4-1), 12-16. doi: 10.11648/j.ijepe.s.2015040401.13

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    AMA Style

    Na-na Li, Hui-ru Zhao, Ming-rui Zhao. Electricity Demand Prediction of Beijing during the 13th Five-year. Int J Energy Power Eng. 2015;4(4-1):12-16. doi: 10.11648/j.ijepe.s.2015040401.13

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  • @article{10.11648/j.ijepe.s.2015040401.13,
      author = {Na-na Li and Hui-ru Zhao and Ming-rui Zhao},
      title = {Electricity Demand Prediction of Beijing during the 13th Five-year},
      journal = {International Journal of Energy and Power Engineering},
      volume = {4},
      number = {4-1},
      pages = {12-16},
      doi = {10.11648/j.ijepe.s.2015040401.13},
      url = {https://doi.org/10.11648/j.ijepe.s.2015040401.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.s.2015040401.13},
      abstract = {With the emergence of a “new economic norm” and the development of “economic integration in Beijing, Tianjin and Hebei”, electricity demand situation in Beijing will change significantly in the future. To guide the planning and construction of power grid in Beijing, it is indispensable to predict electricity demand during the 13th Five-year. Since the factors and affecting mechanisms for electricity demand are different in different sectors, the total electricity consumption in this paper is divided into five parts: the first industry, industry, construction industry, the tertiary industry and resident sectors. The exponential smoothing method and co-integration theory are introduced to establish the forecasting model of electricity demand in different sectors. Therefore, based on the forecasting model and scenario analysis, the analysis results show that the total electricity consumption will grow at an annual rate of 4.9%-6.0% during 13th Five-Year-Plan period, and the consumption would reach more than 0.1397×1012 kWh in 2020.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Electricity Demand Prediction of Beijing during the 13th Five-year
    AU  - Na-na Li
    AU  - Hui-ru Zhao
    AU  - Ming-rui Zhao
    Y1  - 2015/08/03
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ijepe.s.2015040401.13
    DO  - 10.11648/j.ijepe.s.2015040401.13
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
    SP  - 12
    EP  - 16
    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.s.2015040401.13
    AB  - With the emergence of a “new economic norm” and the development of “economic integration in Beijing, Tianjin and Hebei”, electricity demand situation in Beijing will change significantly in the future. To guide the planning and construction of power grid in Beijing, it is indispensable to predict electricity demand during the 13th Five-year. Since the factors and affecting mechanisms for electricity demand are different in different sectors, the total electricity consumption in this paper is divided into five parts: the first industry, industry, construction industry, the tertiary industry and resident sectors. The exponential smoothing method and co-integration theory are introduced to establish the forecasting model of electricity demand in different sectors. Therefore, based on the forecasting model and scenario analysis, the analysis results show that the total electricity consumption will grow at an annual rate of 4.9%-6.0% during 13th Five-Year-Plan period, and the consumption would reach more than 0.1397×1012 kWh in 2020.
    VL  - 4
    IS  - 4-1
    ER  - 

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Author Information
  • School of Economics and Management, North China Electric Power University, Beijing, China

  • School of Economics and Management, North China Electric Power University, Beijing, China

  • School of Economics and Management, North China Electric Power University, Beijing, China

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