Title of article :
A PSO–GA optimal model to estimate primary energy demand of China
Author/Authors :
Shiwei Yu، نويسنده , , Yi-Ming Wei، نويسنده , , Ke Wang، نويسنده ,
Issue Information :
ماهنامه با شماره پیاپی سال 2012
Pages :
12
From page :
329
To page :
340
Abstract :
To improve estimation efficiency for future projections, the present study has proposed a hybrid algorithm, Particle Swarm Optimization and Genetic Algorithm optimal Energy Demand Estimating (PSO–GA EDE) model, for China. The coefficients of the three forms of the model (linear, exponential, and quadratic) are optimized by PSO–GA using factors, such as GDP, population, economic structure, urbanization rate, and energy consumption structure, that affect demand. Based on 20-year historical data between 1990 and 2009, the simulation results of the proposed model have greater accuracy and reliability than other single optimization methods. Moreover, it can be used with optimal coefficients for the energy demand projections of China. The departure coefficient method is applied to get the weights of the three forms of the model to obtain a combinational prediction. The energy demand of China is going to be 4.79, 4.04, and 4.48 billion tce in 2015, and 6.91, 5.03, and 6.11 billion tce (“standard” tons coal equivalent) in 2020 under three different scenarios. Further, the projection results are compared with other estimating methods.
Keywords :
Energy demand estimating , Scenario analysis , PSO–GA optimization algorithm
Journal title :
Energy Policy
Serial Year :
2012
Journal title :
Energy Policy
Record number :
973712
Link To Document :
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