Real-Time Corporate Carbon Footprint Estimation Methodology Based on Appliance Identification

Achieving carbon neutrality is widely recognized as the key measure to mitigate climate change. As the basis for achieving carbon neutrality, corporate carbon footprint (CCF) estimation is mainly based on the disclosed information of corporates to roughly estimate the direct carbon emission, but the estimation may not be comprehensive, timely, and accurate. In this article, the CCF estimation problem is formulated and a novel estimation methodology is proposed for the first time to estimate the direct and indirect carbon emissions of factories in real time. An appliance identification method based on the multihead self-attention mechanism and gated recurrent unit is proposed to identify the device states, and then, calculate the corresponding direct carbon emission. The indirect carbon emission is derived from the electricity consumption of the factory and the marginal carbon emission factor of the connected bus. A dataset containing load and device state data from six different industries is released and used to verify the effectiveness of the proposed method. Experiments show that the proposed appliance identification method is significantly superior to the benchmarks in the literature, and the proposed method can achieve a comprehensive and accurate estimation of the minute-level CCF.

Automated summary

Corporate carbon footprint (CCF) estimation is crucial for achieving carbon neutrality, but current methods may lack comprehensiveness, timeliness, and accuracy. This article proposes a novel method that combines appliance identification and electricity consumption calculation to estimate direct and indirect carbon emissions of factories in real time. Experimental results demonstrate the superiority of the proposed method in appliance identification and its ability to achieve comprehensive and accurate estimation of minute-level CCF.