电力系统自动化2024，Vol.48Issue(1)：2-9,8.DOI:10.7500/AEPS20230314004
基于碳卫星与电力排放数据的碳计量
Carbon Measurement Based on Carbon Satellite and Electricity Emission Data
摘要
Abstract
At present,carbon perception based on greenhouse gas satellite remote sensing technology is gradually becoming a crucial component of new-generation carbon measurement methods.However,accurately extracting carbon emissions data generated by human activities from carbon satellite data represents a key and highly challenging task.In this paper,we propose a novel artificial intelligence algorithm,integrating carbon satellite and electricity emission data,to achieve precise carbon emission measurement.Firstly,we introduce the multimodal data sources used,including carbon satellite and power data,and design corresponding data processing methods.Subsequently,we propose a deep learning method that considers the characteristics of this multimodal data,and construct a data-driven model that reflects the functional relationship among carbon satellite data,power generation data,and carbon source emissions.Finally,based on the carbon concentration remote sensing data from the American OCO-2 carbon satellite and continuous emission monitoring system(CEMS)data from 1 304 American power plants,we validate the effectiveness of the proposed method in the measurement of carbon emissions from power plants.关键词
碳卫星/电力/人工智能/深度学习/碳排放/碳计量Key words
carbon satellite/electricity/artificial intelligence/deep learning/carbon emission/carbon measurement引用本文复制引用
章政文,顾津锦,赵俊华,黄建伟,吴海峰,文福拴..基于碳卫星与电力排放数据的碳计量[J].电力系统自动化,2024,48(1):2-9,8.基金项目
国家自然科学基金资助项目(72171206). This work is supported by National Natural Science Foundation of China(No.72171206). （72171206）
