一个创新的MGM-BPNN-ARIMA模型用于中国的能源消费结构,从组成数据的角度预测
Ruixia Suo1, Qi Wang2, Yuanyuan Tan2
1College of Management, Xi'an University of Science and Technology, Xi'an, 710054, China. suoruixia@xust.edu.cn.
Scientific reports
|April 11, 2024
概括
使用组合数据分析预测中国的能源结构,揭示了一个更加多样化的模式. 然而,天然气和非化石能源的比例可能无法满足未来的政策目标.
科学领域:
- 能源经济学 能源经济学
- 环境科学环境科学
- 数据科学是数据科学.
背景情况:
- 这是中国的.
- 双碳二氧化碳是什么意思
- 这一目标需要准确的能源消耗结构预测.
- 现有的研究往往忽视了能源消耗结构的整体和内部特性.
研究的目的:
- 应用组合数据理论来分析和预测中国的能源消费结构.
- 开发一种新的综合预测模型,整合多种方法.
- 评估中国能源结构的未来发展轨迹及其与政策目标的调整.
主要方法:
- 纳入组合数据分析以捕捉内部结构关系.
- 开发一个最小化艾奇森距离的组合模型,集成新陈代谢灰色模型 (MGM),逆向传播神经网络 (BPNN) 和自行回归集成移动平均线 (ARIMA) 模型.
- 预测2023-2040年期间的能源消耗结构.
主要成果:
- 预计未来的能源消费结构将变得更加多样化.
- 预测表明,到2040年,天然气和非化石能源的比例可能无法实现政府的政策目标.
- 该研究强调了能源组合可能发生重大变化的潜力.
结论:
- 组合数据分析和联合预测模型在能源领域具有很高的适用性.
- 这些发现为适应和改善中国能源消费结构提供了理论见解.
- 政策干预可能需要加速采用天然气和非化石能源.
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