An interpretable privacy-preserving real-time carbon emission estimation approach for heterogeneous industrial enterprises

Accurate monitoring of carbon emissions from high energy-consuming enterprises is foundational to the low-carbon development of the power system. The volume trend and energy consumption structure of diverse industrial enterprises are different. Most current carbon emission accounting relies on annual energy consumption statistics, with one year or more lag. Additionally, there are severe data barriers between various enterprises, considering the consequences of protecting their carbon-related data. This paper proposes an innovative carbon emission monitoring method to address the aforementioned issues. The first step of this method is to construct an accurate and high-frequency carbon emission measurement architecture from the multi-dimensional perspective of data flow and carbon emission flow. Next, three pivotal techniques are employed in hybrid model: variational mode decomposition (VMD), temporal convolutional network (TCN), long short-term memory network with multi-head attention (LSTMMA)—collectively referred to as VMD-TCN-LSTMMA model. The proposed model can decompose data, increase dimension, and extract features of power time series to precisely estimate the direct carbon emissions of industrial enterprises. Moreover, a model training and information sharing framework based on differential privacy federated score weight algorithm (DP-FedSW) is developed to improve monitoring accuracy for enterprises without divulging raw data. A dataset from 142 users in 6 different types of high energy-consuming industries is collected to comprehensively evaluate the proposed monitoring method's performance. Experimental results demonstrate that the proposed method outperforms the conventional strategy, which can improve the stability and effectiveness of the carbon emission estimation model, ensuring the security of raw data.