一个基于分解-整合框架的碳价格区间预测多因素混合模型
Guozhong Zheng1, Kang Li1, Xuhui Yue1
1School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003, China; Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding, 071003, Hebei, China.
Journal of environmental management
|June 8, 2024
概括
这项研究引入了一种新的混合模型,用于准确预测碳价格,提高碳市场效率. 该模型增强了点和间隔预测,为市场参与者提供更好的指导.
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:
- 准确的碳价格估计对于有效的碳交易和减排政策至关重要.
- 现有的预测模型往往缺乏准确性或全面的间隔预测能力.
- 碳市场的波动性和影响因素需要先进的建模技术.
研究的目的:
- 开发一种新的碳价格混合预测模型,包括点和间隔估计.
- 为市场参与者提高碳价格预测的准确性和可靠性.
- 提高碳市场的运营效率,支持减排战略.
主要方法:
- 碳价格的自适应分解使用连续的变化模式分解.
- 通过部分自相关函数和随机森林来为最佳输入变量进行特征选择.
- 混合点预测使用分类增强和内核极端学习机器,通过子搜索算法进行优化.
- 使用自适应带宽核密度估计的间隔预测.
- 通过沙普利的添加式解释模型的可解释性.
主要成果:
- 拟议的混合模型在湖北碳市场数据上实现了高精度,MAE为0.1022,MAPE为0.0022,RMSE为0.1262和R2为0.9921.
- 历史碳价格,布伦特原油期货和欧盟配额期货对碳价格产生了积极影响;Hushen 300显示出负面影响.
- 该模型展示了优越的间隔预测性能,与恒定内核密度估计相比,覆盖率更高,间隔宽度更窄.
结论:
- 开发的混合模型在碳价格预测准确性和可靠性方面取得了重大进展.
- 该模型能够提供准确的点和间隔预测,这有助于市场参与者和政策实施.
- 这种方法可以加强碳市场的运作,并有助于实现气候变化减缓目标.
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