气候变化领域集成服务门户(CCPortal): The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data

CCPortal

DOI	10.1109/ACCESS.2024.3351468
	The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data
	Luo, Ji; Zhuo, Wuyang; Liu, Siyan; Xu, Bingfei
发表日期	2024
ISSN	2169-3536
起始页码	12
卷号	12
英文摘要	With the intensification of global climate change, low-carbon energy has become a hot topic, and governments around the world are implementing corresponding policies to promote its use. This research first establishes a Multi-universe Quantum Harmony Search-Algorithm Dynamic Fuzzy System Ensemble (MUQHS-DMFSE) composite model for carbon emission prediction. This model combines the MUQHS algorithm with the DMFSE method by designing the workflow of the MUQHS algorithm, creating a DMFSE composite prediction model, introducing a sliding factor matrix, and using the MUQHS algorithm to search for the optimal sliding factors, thus obtaining optimized prediction values. In the research on low-carbon economic development, the research applies the Data Envelopment Analysis (DEA) method and establishes Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models to assess the technical efficiency, pure technical efficiency, and scale efficiency of decision-making units. This research also uses the BCC model to project the production frontier and calculate input redundancy and output gap rates, and evaluate low-carbon economic development. Through the establishment and application of these two models, the research achieves carbon emission prediction and low-carbon economic analysis, validating the feasibility of the research methodology. The results show that the composite model can effectively predict carbon emissions, with a Mean Absolute Percentage Error (MAPE) below 3.5% and Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) below 200 tons, demonstrating the feasibility and accuracy of the model. The research on low-carbon economic development in S Province based on the DEA method reveals the need for energy structure adjustment, clean and renewable energy promotion, control of carbon emissions, and optimization of industrial structure with a focus on developing the tertiary industry. Therefore, the use of artificial intelligence and big data analysis can provide more precise insights into the trends and patterns of low-carbon economic development, as well as more effective predictions of future energy demand and resource supply, offering high practical value and scientific significance.
英文关键词	Low-carbon energy; MUQHS-DMFSE composite model; low-carbon economy; energy demand; resource supply
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001157866300001
来源期刊	IEEE ACCESS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304690
作者单位	Shanghai Polytechnic University; People's Bank of China
推荐引用方式 GB/T 7714	Luo, Ji,Zhuo, Wuyang,Liu, Siyan,et al. The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data[J],2024,12.
APA	Luo, Ji,Zhuo, Wuyang,Liu, Siyan,&Xu, Bingfei.(2024).The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data.IEEE ACCESS,12.
MLA	Luo, Ji,et al."The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data".IEEE ACCESS 12(2024).