Enterprise carbon emission prediction algorithm based on multidimensional characteristic data

• Main Authors: Li, Xiong, Zhou, Ling, Ji, Yuan, Yu, Siwu, Zou, Jun
• Format: Conference Proceeding
• Language: English
• Published: SPIE 26.05.2023
• ISBN: 9781510666160; 1510666168
• ISSN: 0277-786X
• DOI: 10.1117/12.2682323

Aiming at the problem that the carbon emissions of enterprises change with the change of production mode, which leads to the deviation of enterprises in the process of carbon emissions prediction and affects the accuracy of enterprise carbon emissions prediction, an enterprise carbon emission prediction algorithm based on multi-dimensional feature data is proposed. Analyze the influencing factors of carbon emissions of enterprises, collect multi-dimensional data of the influencing factors of carbon emissions through automatic collection and manual filling, and pre-process them. The principal component analysis and kernel function algorithm are used to extract multidimensional data features, and the extracted multidimensional features are transferred to the Drosophila - differential evolution optimization RBF neural network model, and the best enterprise carbon emission prediction results are obtained through continuous learning of the model. The experimental results show that the proposed algorithm can effectively predict the total carbon emissions of enterprises in high and low emission scenarios each month, accurately predict the carbon emissions during production, processing and combustion, and predict the emissions of atmospheric pollutants such as nitrogen oxides and inhalable particulates in the process of carbon emissions of enterprises, with good prediction effect.