Industrial Carbon Footprint (ICF) Calculation Approach Based on Bayesian Cross-Validation Improved Cyclic Stacking

Achieving carbon neutrality is widely regarded as a key measure to mitigate climate change. The industrial carbon footprint (ICF) calculation, as a foundation to achieve carbon neutrality, primarily relies on roughly estimating direct carbon emissions based on information disclosed by industries. However, these estimates may not be comprehensive, timely, and accurate. This paper elaborates on the issue of ICF calculation, dividing a factory's carbon emissions into carbon emissions directly produced by appliances and electricity consumption carbon emissions, to estimate the total carbon emissions of the factory. An appliance identification method is proposed based on a cyclic stacking method improved by Bayesian cross-validation, and an appliance state correction module SHMM (state-corrected hidden Markov model) is added to identify the state of the appliance and then to calculate the corresponding appliance carbon emissions. Electricity consumption carbon emissions come from the factory's electricity consumption and the marginal carbon emission factor of the connected bus. Regarding the selection of artificial intelligence models and cross-validation technique required in the appliance identification method, this paper compares the effects of 7 cross-validation techniques, including stratified K-fold, K-fold, Monte Carlo, etc., on 14 machine learning algorithms such as AdaBoost, XGBoost, feed-forward network, etc., to determine the technique and algorithms required for the final appliance identification method. Experiment results show that the proposed appliance identification method estimates device carbon emissions with an error of less than 3%, which is significantly superior to other models, demonstrating that the proposed approach can achieve comprehensive and accurate ICF calculation.