Energy demand projection of China using a path-coefficient analysis and PSO–GA approach

Energy demand projection is fundamental to rational energy planning formulation. The present study investigates the direct and indirect effects of five factors, namely GDP, population, proportion of industrial, proportion of urban population and coal percentage of total energy consumption on China’s energy demand, implementing a path-coefficient analysis. On this basis, a hybrid algorithm, Particle Swarm Optimization and Genetic Algorithm optimal Energy Demand Estimating (PSO–GA EDE) model, is proposed for China. The coefficients of the three forms of the model (linear, exponential and quadratic model) are optimized by proposed PSO–GA. To obtain a combinational prediction of three forms, a departure coefficient method is applied to get the combinational weights. The results show that the China’s energy demand will be 4.48 billion tce in 2015. Furthermore; the proposed method forecast shows its superiority compared with other single optimization method such as GA, PSO or ACO and multiple linear regressions.

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