A Novel Carbon Emission Calculation Method for Power System Based on Personalized Transformer with Two-Stage Training

Accurately and comprehensively calculating carbon emissions in the power system is a fundamental prerequisite for achieving low-carbon energy transitions. Existing carbon flow theory-based methods primarily concentrate on emissions from the grid and load sides, while the methods for generation-side emissions often rely on costly continuous monitoring systems or imprecise default emission factors. However, in practice, most power generation units cannot achieve real-time and precise carbon emission measurement, leading to generation side data deviations that affect overall computational accuracy. To address the limitations, this paper proposes a novel carbon emission measurement method based on heterogeneous data and personalized Transformer, applicable to various power generation units. This method has several key innovations: ① extends traditional total electricity production based models are extended to a multi-feature framework to capture similarities and differences in time-series data among various generator units; ② an improved Transformer is designed that integrates short-term relationship extraction, long-term differential identification and long-term and short-term feature fusion modules to enhance the multi-feature based emission mapping process, and ③ a two stage training protocol is adopted, with self-supervised pretraining of feature extractors followed by fine tuning, to accelerate convergence and improve accuracy. Experiments on real-world generation-unit data show that the proposed method reduces average RMSE by 22.3% relative to a standard Transformer and by 15.9% relative to Informer. Further validation utilizing the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test case confirms the effectiveness and applicability of the model for carbon emission measurement across all segments of the power system.