气候变化领域集成服务门户(CCPortal): A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price

CCPortal

DOI	10.3389/fenrg.2023.1341881
	A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price
	Li, Zejun; Long, Jun; Li, Lue
发表日期	2024
ISSN	2296-598X
起始页码	11
卷号	11
英文摘要	Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.
英文关键词	carbon trading price forecasting; machine learning model; mixed frequency forecasting; multi-objective optimization; ensemble forecasting
语种	英语
WOS研究方向	Energy & Fuels
WOS类目	Energy & Fuels
WOS记录号	WOS:001148481000001
来源期刊	FRONTIERS IN ENERGY RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299430
作者单位	Guangxi University; Guangxi University of Finance & Economics; Guangxi University of Finance & Economics
推荐引用方式 GB/T 7714	Li, Zejun,Long, Jun,Li, Lue. A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price[J],2024,11.
APA	Li, Zejun,Long, Jun,&Li, Lue.(2024).A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price.FRONTIERS IN ENERGY RESEARCH,11.
MLA	Li, Zejun,et al."A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price".FRONTIERS IN ENERGY RESEARCH 11(2024).