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Research on the evolution model of an energy supply–demand network

Author

Listed:
  • Sun, Mei
  • Zhang, Pei-Pei
  • Shan, Tian-Hua
  • Fang, Cui-Cui
  • Wang, Xiao-Fang
  • Tian, Li-Xin

Abstract

A universal bipartite model is proposed based on an energy supply–demand network. The analytical expression of SPL distribution of the node weight, the “shifting coefficient” α and the scaling exponent γ are presented without edge weight growth by using the mean-field theory approach. The numerical results of SPL distribution of the node weight, the “shifting coefficient” α and the scaling exponent γ with edge weight growth are also presented. The production’s SPL distribution of the US coal enterprizes from 1991 to 2009 is obtained from the empirical analysis. The numerical results obtained from the model are in good agreement with the empirical results.

Suggested Citation

  • Sun, Mei & Zhang, Pei-Pei & Shan, Tian-Hua & Fang, Cui-Cui & Wang, Xiao-Fang & Tian, Li-Xin, 2012. "Research on the evolution model of an energy supply–demand network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4506-4516.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:19:p:4506-4516
    DOI: 10.1016/j.physa.2012.04.028
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    References listed on IDEAS

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    1. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    2. Dahui, Wang & Li, Zhou & Zengru, Di, 2006. "Bipartite producer–consumer networks and the size distribution of firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 359-366.
    3. Li, Menghui & Fan, Ying & Chen, Jiawei & Gao, Liang & Di, Zengru & Wu, Jinshan, 2005. "Weighted networks of scientific communication: the measurement and topological role of weight," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 643-656.
    4. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    5. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
    6. Kahouli, Sondès, 2011. "Re-examining uranium supply and demand: New insights," Energy Policy, Elsevier, vol. 39(1), pages 358-376, January.
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    Cited by:

    1. Mir Hossein Mousavi, 2015. "An Estimation of Natural Gas Demand in Household Sector of Iran; the Structural Time Series Approach," Proceedings of International Academic Conferences 2804383, International Institute of Social and Economic Sciences.
    2. Zhao, Zhen-yu & Zhu, Jiang & Xia, Bo, 2016. "Multi-fractal fluctuation features of thermal power coal price in China," Energy, Elsevier, vol. 117(P1), pages 10-18.
    3. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    4. Zhang, Chi & Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2017. "Exploring the transformation and upgrading of China’s economy using electricity consumption data: A VAR–VEC based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 144-155.

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