基于CEEMDAN-VMD二次分解和BiLSTM的碳价格预测研究
Ming Fang1, Yuanliang Zhang1, Wei Liang1
1Guangzhou Power Supply Bureau of Guangdong Grid Co., GuangZhou, 510600, Guangdong, China.
Environmental science and pollution research international
|March 17, 2025
概括
准确的碳价格预测对于发展碳市场至关重要. 结合CEEMDAN-VMD分解和COA优化的BiLSTM网络的新型模型显著提高了主要市场的预测准确性.
科学领域:
- 环境经济学环境经济学
- 计算金融是指计算金融.
- 气候变化缓解缓解 气候变化缓解
背景情况:
- 碳市场是经济有效地缓解气候变化的关键机构创新.
- 准确的碳价格预测对于市场发展,投资和政策至关重要.
- 现有的预测模型可能缺乏复杂碳市场动态所需的精度.
研究的目的:
- 提出一个先进的碳价格预测模型.
- 在碳市场中增强特征提取和预测准确性.
- 为指导碳投资和市场稳定提供可靠的工具.
主要方法:
- 适应性白噪声完整组合实证模式分解 (CEEMDAN) 用于初始信号分解.
- 变化模式分解 (VMD) 用于进一步精制高频组件.
- 双向长期短期记忆 (BiLSTM) 网络,由子算法 (COA) 优化进行预测.
主要成果:
- 在CEEMDAN-VMD-COA-BiLSTM模型中,预测误差很低:0.173% (欧盟排放交易体系),0.111% (CCETE) 和0.635% (BEA).
- 二次分解增强了特征提取和碳价格序列的规律性.
- 对COA的优化改善了BiLSTM网络的稳定性和可靠性.
结论:
- 拟议的模型在全球主要碳市场中显示出高度的适用性和准确性.
- 这种方法为碳市场的稳定发展提供了有价值的参考.
- 有效的碳价格预测支持通过市场机制减少排放的战略.
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