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

目前,基于温室气体卫星遥感探测技术的碳感知正逐步成为新一代碳计量方法的重要组成部分。然而,从碳卫星数据中准确提取由人类活动产生的碳排放数据是一项至关重要且极具挑战性的任务。文中结合碳卫星与电力排放数据,提出一种新的人工智能算法,以实现准确的碳排放计量。首先,介绍了所使用的包括碳卫星和电力数据在内的多模态数据源,并设计了相应的数据筛选方法。然后,提出了考虑此多模态数据特性的深度学习方法,构建反映碳卫星数据、发电量数据与碳源碳排放量之间函数关系的数据驱动模型。最后,基于美国OCO-2碳卫星的碳浓度遥感数据及美国1304家火力发电厂的连续烟气监测系统数据验证了所提方法在发电厂碳排放量计量问题上的有效性。

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.