通过混合分解-化建模改进了短期碳排放预测
Peng Zhang1,2
1School of Statistics, Shanxi University of Finance and Economics, Taiyuan, 030006, China. zhangp@tit.edu.cn.
Scientific reports
|October 16, 2025
概括
准确的碳排放预测对于中国的气候目标至关重要. 使用CEEMDAN,EWT和LSTM的新分解-排泄-整合模型提高了温室气体排放的短期预测准确度.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气候建模气候模型
背景情况:
- 碳排放是一个重大的全球环境挑战.
- 准确的温室气体排放预测对于实现国家气候目标至关重要,例如中国的"双碳"目标.
- 现有的预测方法难以应对碳排放数据的复杂性和非静止性.
研究的目的:
- 为增强短期碳排放预测开发一种新的混合模型.
- 为了提高复杂,非静止时间序列数据的预测的准确性和概括性.
- 为支持气候政策和减排战略提供一个强大的工具.
主要方法:
- 一个"分解-去除-整合"框架,将完整的集体实证模式分解与适应噪声 (CEEMDAN),实证波束转换 (EWT) 和长短期记忆 (LSTM) 神经网络相结合.
- CEEMDAN将碳排放时间序列分解为多尺度内在模式函数 (IMF) 和残余序列.
- EWT通过自适应性处理高频IMF来消除和重建数据,而LSTM模型在集成之前捕捉每个组件内的时间依赖.
主要成果:
- 与七个基线模型相比,拟议的分解-去除-整合模型表现出优越的性能.
- 在关键指标中观察到显著的改善:平均绝对百分比误差 (MAPE),根平均平方误差 (RMSE),平均绝对误差 (MAE) 和R平方 (R2).
- 该模型有效地捕获了复杂的碳排放数据中的详细特征和趋势模式.
结论:
- 协同作用的"分解-去除-整合"方法提高了短期碳排放预测的分析精度.
- 这种混合模型为预测非静态环境数据提供了更准确,更可靠的方法.
- 这些发现为实现有效的温室气体排放管理和气候政策实施提供了宝贵的贡献.
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