气候变化领域集成服务门户(CCPortal): Real-Time Corporate Carbon Footprint Estimation Methodology Based on Appliance Identification

CCPortal

DOI	10.1109/TII.2022.3154467
	Real-Time Corporate Carbon Footprint Estimation Methodology Based on Appliance Identification
	Liu, Guolong; Liu, Jinjie; Zhao, Junhua; Qiu, Jing; Mao, Yiru; Wu, Zhanxin; Wen, Fushuan
发表日期	2023
ISSN	1551-3203
EISSN	1941-0050
起始页码	1401
结束页码	1412
卷号	19 期号:2
英文摘要	Achieving carbon neutrality is widely recognized as the key measure to mitigate climate change. As the basis for achieving carbon neutrality, corporate carbon footprint (CCF) estimation is mainly based on the disclosed information of corporates to roughly estimate the direct carbon emission, but the estimation may not be comprehensive, timely, and accurate. In this article, the CCF estimation problem is formulated and a novel estimation methodology is proposed for the first time to estimate the direct and indirect carbon emissions of factories in real time. An appliance identification method based on the multihead self-attention mechanism and gated recurrent unit is proposed to identify the device states, and then, calculate the corresponding direct carbon emission. The indirect carbon emission is derived from the electricity consumption of the factory and the marginal carbon emission factor of the connected bus. A dataset containing load and device state data from six different industries is released and used to verify the effectiveness of the proposed method. Experiments show that the proposed appliance identification method is significantly superior to the benchmarks in the literature, and the proposed method can achieve a comprehensive and accurate estimation of the minute-level CCF.
英文关键词	Appliance identification; artificial intelligence; carbon neutrality; industrial appliance identification dataset (IAID); real-time corporate carbon footprint (CCF) estimation
语种	英语
WOS研究方向	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000926964700027
来源期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281432
作者单位	The Chinese University of Hong Kong, Shenzhen; The Chinese University of Hong Kong, Shenzhen; Shenzhen Institute of Artificial Intelligence & Robotics for Society; Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; University of Sydney; The Chinese University of Hong Kong, Shenzhen; Zhejiang University
推荐引用方式 GB/T 7714	Liu, Guolong,Liu, Jinjie,Zhao, Junhua,et al. Real-Time Corporate Carbon Footprint Estimation Methodology Based on Appliance Identification[J],2023,19(2).
APA	Liu, Guolong.,Liu, Jinjie.,Zhao, Junhua.,Qiu, Jing.,Mao, Yiru.,...&Wen, Fushuan.(2023).Real-Time Corporate Carbon Footprint Estimation Methodology Based on Appliance Identification.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,19(2).
MLA	Liu, Guolong,et al."Real-Time Corporate Carbon Footprint Estimation Methodology Based on Appliance Identification".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 19.2(2023).