基于分解技术和极端梯度提升的碳价格预测,由灰狼优化算法优化.
Mengdan Feng1, Yonghui Duan2, Xiang Wang3
1Department of Civil Engineering, Henan University of Technology, No. 100, Lianhua Street, Gaoxin District, Zhengzhou, 450001, China. mengdanfeng139499@foxmail.com.
Scientific reports
|October 27, 2023
概括
精确的碳价格预测对于减少二氧化碳排放至关重要. 一种结合灰狼优化器 (GWO),极端梯度增强 (XGBOOST) 和完整集体实证模式分解与自适应噪声 (CEEMDAN) 的新型混合模型显著提高了预测准确度.
科学领域:
- 环境科学 环境科学
- 计算经济学计算经济学
- 机器学习 机器学习
背景情况:
- 精确的碳价格预测对于有效减少二氧化碳排放和减缓全球变暖至关重要.
- 单个机器学习模型通常在预测碳价格方面表现出局限性,原因是固有的复杂性.
- 确定关键的碳价格指标对于开发可靠的预测模型至关重要.
研究的目的:
- 提出一种新的混合碳价格预测模型 (GWO-XGBOOST-CEEMDAN),可以克服单一模型的局限性.
- 提高碳价格预测的准确性和可靠性.
- 提供一种有效的方法,用于预测未来的碳价格在排放交易市场.
主要方法:
- 利用随机森林 (RF) 选初级碳价格指标并确定影响因素.
- 开发了一个灰狼优化器 (GWO) 优化了极端梯度增强 (XGBOOST) 模型 (GWO-XGBOOST).
- 应用完整集体实证模式分解与适应噪声 (CEEMDAN) 来分解和纠正GWO-XGBOOST模型的残留物,创建GWO-XGBOOST-CEEMDAN模型.
主要成果:
- 拟议的GWO-XGBOOST-CEEMDAN模型在实验预测中显示出更高的预测精度.
- 混合模型在预测广东,湖北和福建排放交易市场的碳价格方面表现优于比较模型.
- 该研究验证了GWO,XGBOOST和CEEMDAN综合方法在碳价格预测方面的有效性.
结论:
- 与传统方法相比,GWO-XGBOOST-CEEMDAN模型为碳价格预测提供了一种优越的方法.
- 混合模型提供了一种可靠和有效的实验方法来预测未来的碳价格.
- 使用先进的机器学习技术准确预测碳价格对于缓解气候变化的努力至关重要.
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