Carbon trading behavior of power generation enterprises based on causal inference

Abstract

To realize the prediction of carbon trading volume of power generation enterprises, this paper takes a power generation enterprise as an example, and introduces the causal inference theory to study the carbon trading behavior of enterprises. Data mining technology is introduced to obtain the operation data of power generation enterprises in the market. It is also used to collate the obtained multi-channel data, and cluster them according to the data category to extract the attribute characteristics of carbon trading behavior of the power generation enterprises. Based on the causal reasoning relationship and referring to the SEM model in the causal reasoning relationship, the carbon trading behavior model is constructed. According to the market access conditions of sellers and sellers, the balance of power supply and demand is taken as the starting point to predict the carbon trading volume of power generation enterprises and complete the analysis of carbon trading behavior of power generation enterprises. Taking a power generation enterprise as an example, related application experiments are designed. The experimental results show that the prediction results are reliable by using the designed method to predict the carbon trading volume of enterprises, which can be used as the key basis for the decision-making of carbon trading behavior of power generation enterprises.