基于分解-重建-整体框架,预测中国的区间碳价格
1School of Economics and Management, Anhui University of Science and Technology, Huainan, China. bbhu@aust.edu.cn.
Carbon balance and management
|January 9, 2026
概括
准确的碳价格预测对于绿色能源转型至关重要. 这项研究引入了一种新的框架,使用多变量变化模式分解和极端学习机器来预测间隔碳价格,改善市场管理.
科学领域:
- 环境经济学环境经济学
- 计算金融是指计算金融.
- 数据科学数据科学数据科学
背景情况:
- 有效的碳市场管理和全球绿色能源转型需要精确的碳价格预测.
- 现有的分解和重建方法与区间值碳价格的复杂性作斗争.
- 准确的间隔碳价格预测对于环境政策和能源市场的知情决策至关重要.
研究的目的:
- 开发一个先进的框架,准确预测区间值的碳价格.
- 解决传统方法在处理碳价格波动性和间隔性质方面的局限性.
- 通过纳入外部影响因素,提高碳价格预测模型的稳定性和准确性.
主要方法:
- 提出了一个新的分解-重建-整体框架.
- 通过Rime Ice优化算法 (RIME) 优化的多变量变化模式分解 (MVMD) 用于分解和重建.
- 多尺度模糊分散 (MFDE) 被用于增强信号处理.
- 基于RIME优化的多个内核的极端学习机器 (MKELM) 用于子序列预测.
- 外部因素 (能源,经济,环境) 被整合到预测模型中.
主要成果:
- 与基准方法相比,拟议的模型显示出更高的预测准确性.
- 经验分析证实了该模型在预测间隔碳价格方面的稳定性.
- 该框架有效地捕捉了区间估值碳价格数据中固有的波动.
- 外部因素的整合显著改善了预测性能.
结论:
- 开发的分解-重建-整体框架在间隔碳价格预测方面取得了重大进展.
- 该模型的准确性和稳定性使其适用于碳市场中复杂的现实场景.
- 这种方法为政策制定者和市场参与者在绿色能源过渡过程中提供了宝贵的工具.
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