Carbon Emission Anomaly Monitoring Method Based on Multidimensional Clustering and Intelligent Analysis

In order to achieve the accurate monitoring and anomaly identification of carbon emissions in complex multiscenarios, this paper proposes a carbon emission anomaly monitoring method based on multi-dimensional clustering and intelligent analysis. Firstly, a carbon-electric coupling measurement model is designed to support multi-scenario data acquisition and real-time carbon emission calculation. Secondly, a carbon emission calculation model suitable for different scenarios such as buildings, commercial and photovoltaic power generation is constructed. Then, a multi-dimensional clustering analysis algorithm is designed to achieve multi-dimensional carbon emission data visualization and decision support by year, month and working day. Finally, based on the Isolated Forest anomaly detection algorithm, optimization suggestions are generated to reduce the carbon emission level. The case study shows that the proposed method can effectively identify carbon emission anomalies in multiple scenarios, generate optimization suggestions and significantly reduce carbon emissions, providing intelligent technical support for carbon control.