Abstract
Monitoring carbon emissions, as well as evaluating and controlling the level of carbon emissions, are important prerequisites for combating climate change and promoting sustainable development. In this paper, real-time estimation of carbon emissions based on non-intrusive electricity load monitoring is investigated, existing machine learning approaches and related issues of load identification and marginal carbon emission factors are elucidated, and existing carbon emission monitoring approaches are analyzed and summarized. The methods of real-time carbon emission measurement are summarized, and their development is anticipated. Most of the existing methods for measuring and analyzing carbon emissions in power systems are unable to meet the development needs of real-time carbon emission calculation and have the disadvantages of higher installation and maintenance costs and poorer economy of monitoring equipment. Therefore, in this paper, a real-time carbon emission monitoring scheme based on deep learning is used to estimate carbon emissions by dividing them into direct carbon emissions and indirect carbon emissions.
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