[基于双方向分解的非线性综合短期碳排放预测]
Xiao-Jun Zhu1, Gui-Yuan Xue1, Zheng Xu1
1Economic and Technical Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China.
Huan jing ke xue= Huanjing kexue
|October 15, 2025
概括
本研究引入了一种新的短期碳排放预测模型,利用双模式分解和非线性集成. 这种方法通过有效分析挥发性排放数据来提高预测准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 短期碳排放数据具有很高的波动性,这对准确的预测构成了挑战.
- 现有的预测模型可能难以捕捉碳排放波动的复杂动态.
研究的目的:
- 开发一个改进的短期碳排放预测模型.
- 通过解决数据波动和整合各种建模方法来提高预测准确度.
主要方法:
- 采用CEEMDAN和RIME-VMD的双模式分解来提取多尺度组件.
- 使用LSTM,GRU和KELM来预测分解的组件.
- 应用XGBoost集体学习用于最终预测合成.
主要成果:
- 双模式分解有效地捕获了全面的数据信息.
- 不同的分解方法之间实现了互补的优势.
- 非线性集成成功合成了多样化的预测模型,提高了准确性.
结论:
- 拟议的模型在短期碳排放预测方面表现出卓越的性能.
- 双模式分解和非线性集成是处理波动性环境数据的有效策略.
- 该模型有效地提取和利用信息,从而提高了预测准确性.
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