A PSO–GA optimal model to estimate primary energy demand of China

• Published in: Energy policy Vol. 42; pp. 329 - 340
• Main Authors: Yu, Shiwei, Wei, Yi-Ming, Wang, Ke
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2012; Elsevier; Elsevier Science Ltd
• Subjects: Algorithms; Applied sciences; China; Coal; Coefficients; Data; Data analysis; Demand; Demand analysis; Economic data; Economic structure; Energy; Energy consumption; Energy demand; Energy demand estimating; Energy economics; Energy policy; Energy resources; Estimation; Exact sciences and technology; General, economic and professional studies; Genetic algorithms; Methodology. Modelling; Modelling; Optimization; Optimization algorithms; Power demand; prediction; primary energy; Projections; PSO–GA optimization algorithm; Reliability; Scenario analysis; Simulation; Studies; system optimization; Urbanization; China; Asia; China, People's Rep; PSO–GA optimization algorithm; Scenario analysis; Energy demand estimating; Script; Methodology; Genetic algorithm; PSO-GA optimization algorithm; Energy demand; 2010-2020; Modeling; Particle swarm optimization; Forecasting
• ISSN: 0301-4215; 1873-6777
• DOI: 10.1016/j.enpol.2011.11.090

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. ► A hybrid algorithm PSO–GA optimal energy demands estimating model for China. ► Energy demand of China is estimated by 2020 in three different scenarios. ► The projection results are compared with other estimating methods.