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Abstract：
Effective forecasting of energy consumption structure is vital for China to reach its \"dual carbon\" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023-2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China.
摘要：
有效预测能源消费结构对我国实现"双碳"目标至关重要。然而,关于能源消费结构的整体性和内在性质的现有研究很少受到关注。因此,本文将成分数据理论纳入能源消费结构研究，这不仅考虑了结构内部特征的特殊性，但也更深入地挖掘相关信息。同时,基于组合数据中Aitchison距离平方的最小化理论，基于三个单一模型的组合模型，即新陈代谢灰色模型(MGM)，反向传播神经网络(BPNN)模型，和自回归积分移动平均(ARIMA)模型，是本文的结构。2023-2040年能源消费结构预测结果表明,未来我国能源消费结构将朝着更加多元化的方向发展,但是天然气和非化石能源的比例尚未达到政府设定的政策目标。本文不仅表明联合预测模型的成分数据在能源领域具有很高的适用性,对适应和改善我国能源消费结构具有一定的理论意义。
