High-Frequency Carbon Emission Monitoring  for Power Generation Enterprises Based on ICEEMDAN- Inception-Transformer

Abstract

Accurate carbon accounting is essential for power generation enterprises to participate in carbon markets and implement carbon reduction strategies. However, due to excessive reliance on detailed energy data and manual calculations, carbon emission accounting in power generation enterprises suffers from low frequency, significant lag, and poor reliability. Some evidences suggest a strong correlation between internal carbon emissions and electricity consumption in power generation enterprises. Inspired by them, this paper proposes a novel model, named ICEEMDAN-Inception-Transformer, to thoroughly explore the relationship between power data and carbon emissions, providing precise hourly carbon emission acquisition for power enterprises. This model first utilizes ICEEMDAN to extract the significant characteristics of power data, then employs advanced Inception and Transformer structures to capture the complex high-dimensional features of the "electricity-carbon" correlation, thereby realizing enterprise carbon emissions monitoring. The model was extensively validated on three datasets from three different types of power enterprises. The average performance on indicators of RMSE, MAE, MAPE, and R2 of the model on the three datasets reached 11.69 tCO2, 9.58 tCO2, 2.44%, and 96.42%, respectively. The results demonstrate that the proposed monitoring model possesses certain advantages in terms of the accuracy and robustness of acquiring enterprise carbon emissions, providing valuable insights for high-frequency accurate carbon monitoring in power generation enterprises.