气候变化领域集成服务门户(CCPortal): Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers

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DOI	10.1016/j.asoc.2024.111639
	Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers
	Wu, Binrong; Zeng, Huanze; Wang, Zhongrui; Wang, Lin
发表日期	2024
ISSN	1568-4946
EISSN	1872-9681
起始页码	159
卷号	159
英文摘要	Carbon emissions play a pivotal role in exacerbating the global warming crisis and driving climate change. Accurate and consistent projections of carbon emissions are of utmost importance for nations worldwide, as they shape emission reduction strategies and expedite the pursuit of carbon peaking and carbon neutrality goals. While previous studies have focused on hybrid methodologies for carbon emission forecasting, yielding commendable predictive performance, these approaches often overlook the significance of internal interpretability within the forecasting models. In light of this gap, this study introduces a groundbreaking and elucidating hybrid carbon emission forecasting model that amalgamates the two-stage layer decomposition method, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT). To begin with, a series of sub-sequences is derived by employing a flexible two-stage decomposition strategy, which leverages a linear-nonlinear decomposition criterion to thoroughly extract the fluctuating characteristics inherent in the carbon emission series. Subsequently, the JADE algorithm intelligently and efficiently optimizes the parameter combinations within the TFT model, ensuring both stability and reliability of the prediction framework. Empirical investigations conclusively demonstrate the remarkable applicability and efficacy of the proposed model in short-term carbon emission forecasting. By delving into the interpretability of the model's results, the study enhances the capacity of policymakers to devise well-informed strategies based on comprehensive insights gleaned from the forecasting process.
英文关键词	Carbon dioxide emissions forecasting; Interpretable forecasting model; Decomposition method; Evolutionary algorithm; Deep learning
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001237720000001
来源期刊	APPLIED SOFT COMPUTING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297411
作者单位	Hohai University; Wuhan Institute of Technology; Huazhong University of Science & Technology
推荐引用方式 GB/T 7714	Wu, Binrong,Zeng, Huanze,Wang, Zhongrui,et al. Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers[J],2024,159.
APA	Wu, Binrong,Zeng, Huanze,Wang, Zhongrui,&Wang, Lin.(2024).Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers.APPLIED SOFT COMPUTING,159.
MLA	Wu, Binrong,et al."Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers".APPLIED SOFT COMPUTING 159(2024).