Carbon emission monitoring based on analysis from “electricity-carbon” relationship of cement enterprises
ZHANG Shu-han1, CHEN Hui1, WANG Bin2, YU Run-xin3, MA Zhi-ting4, MIAO Yu-han4
1. Energy Development Research Institute, China Southern Power Grid, Guangzhou 510700, China; 2. China Southern Power Grid, Guangzhou 510700, China; 3. Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China; 4. Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
Abstract：Based on the advantages of electrical data-instantaneity, accuracy and wide coverage, this paper establishes the electricity-carbon index for cement enterprises. By exploring the relationship between purchased electricity and total carbon dioxide emissions using the machine learning method, we formulate and investigate both single-sample daily monitoring models and multiple-sample annual monitoring models. The numerical study demonstrates that the Lasso model outperforms the other nine regression models deployed in the monitoring models, with a goodness of fit (R2) of 0.915 and 0.831. The results indicate that the critical factors influencing the electricity-carbon index of cement enterprises are the electricity emission factors in the clinker production and the proportion of electricity consumption in the clinker section, thus underscoring the importance of electrical data in carbon emission monitoring. The proposed model has the potential to significantly improve the efficiency and reduce the cost of carbon emission monitoring for cement enterprises.
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