Monitoring Carbon Emission From Industrial Users by Identifying Multistate Load in Advanced Metering Infrastructure

Carbon monitoring is critical in reducing carbon emissions, especially as the increasing electricity consumption in industrial processes offers a unique opportunity to monitor emissions from the electrical perspective, enabling carbon emission monitoring through electrical load monitoring. However, current methods have proven ineffective in providing high-precision monitoring due to the neglect of the individual equipment’s and their different states’ differing contributions to carbon emissions. Therefore, we propose a carbon emission monitoring method based on multistate load identification in advanced metering infrastructure (AMI), which incorporates both the gram component and the residual component. The gram component converts load series into images to acquire more features, while the residual component identifies the multistate devices and monitors carbon emission, accurately. In addition, a novel loss based on focal loss and Huber loss is designed to minimize carbon emission monitoring errors. This study presents a model that excels in both load identification and carbon emission monitoring. Experiments are conducted using industrial users from the IAID dataset, with performance evaluated using metrics such as ACC, $F1_{\text {macro}}$ , $F1_{\text {micro}}$ , and carbon monitoring error ( $\text {AEE}_{\text {car}}$ ). The proposed method outperforms existing approaches, demonstrating superior accuracy in emission monitoring, with the $\text {AEE}_{\text {car}}$ below 5%.