The Carbon Emission Accounting and Prediction of the Power Generation Side based on LSTM in Jilin Province
Main Article Content
Abstract
In the context of global warming and the dramatic increase in greenhouse gas emissions, the power industry is the largest source of carbon emissions. Adjusting and optimizing the carbon dioxide emissions from the power industry will help China achieve its “dual carbon” goals and is of great significance for mitigating global carbon dioxide emissions. This paper takes six power plants in Jilin Province as the research objects, and firstly accounts for the carbon emission production data between January 2020 and December 2023 according to the "Accounting Methods and Reporting Guidelines for Greenhouse Gas Emissions from Enterprises - Power Generation Facilities". Then, LSTM was used to establish carbon emission prediction models for six different power plants in Jilin Province, and the analysis of each model showed that that the single-step prediction RMSEs are all less than one, with higher prediction accuracy, but only can used in short-term prediction, the multi-step prediction RMSEs are bigger than one, with lower prediction accuracy, but can used in long-term carbon emission trend prediction can be achieved. The carbon emission trend prediction of six power plants in Jilin province between January 2024 and August 2031 confirms that the carbon emissions of power plants will be affected by seasons and shown cyclical changes. Finally, reasonable policy recommendations are provided for the successful realisation of the "double carbon" target for electricity in Jilin Province.
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