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

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.

[1]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Ying Fan,et al.  Weighted networks of scientific communication: the measurement and topological role of weight , 2005 .

[3]  Fang Jin-Qing,et al.  Bielemental random networks , 2009 .

[4]  Réka Albert,et al.  Structural vulnerability of the North American power grid. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Tao Zhou,et al.  Optimal transport on supply-demand networks. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Wang Dahui,et al.  Bipartite Producer-Consumer Networks and the Size Distribution of Firms , 2006 .

[8]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[9]  Matthieu Latapy,et al.  Basic notions for the analysis of large two-mode networks , 2008, Soc. Networks.

[10]  Sondès Kahouli.  Re-examining uranium supply and demand: New insights , 2011 .

[11]  Ali Azadeh,et al.  An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments , 2010 .

[12]  William E. Roper,et al.  Energy demand estimation of South Korea using artificial neural network , 2009 .

[13]  M. Zhang,et al.  Forecasting the transport energy demand based on PLSR method in China , 2009 .