Abstract: The electric power industry is a key sector for carbon emission control. Accurate and real-time accounting of carbon emissions in the power industry is the basis for supporting the carbon reduction of power industry. At present, the measurement of carbon emissions in the power industry is mainly based on the actual measurement method or the accounting method, which is difficult to balance low measurement costs and real-time measurement capabilities. To this end, this paper fully considers the good power data foundation of the power industry, fully explores the correlation between electricity and carbon, and proposes a short-term electricity-to-carbon method in the power industry based on machine learning methods based on historical data of electricity. In this method, feature extraction is conducted with Convolutional Neural Networks (CNNs), and the Light Gradient Boosting Machine (LightGBM) is used for carbon emission estimation based on extracted features. Moreover, K-Fold cross-validation is used in model training, and parameter optimization is performed using grid search to enhance the model's generalization capability and robustness. To validate the effectiveness of the proposed model, other machine learning models were tested under the same data segmentation on daily and hourly data sets, for model performance evaluation. The results indicate that the proposed model outperforms other models in both performance evaluation and the consistency between estimated and target values.

Key words: electric folding carbon, convolutional neural networks, light gradient boosting machine, carbon emissions, machine learning, combined model