Hybrid Fusion Model for Real-Time CO ₂ Emission Tracking and Electricity Consumption Monitoring Using Power Big Data

Real-time carbon emission monitoring is one of the essential features to protect the environment in unsafe conditions. This study considers this a primary background and establishes a practical hybrid approach to meet these limitations. The proposed research is a fusion model that combines the strengths of the input and output models. It was effectively analyzed and implemented in the existing, as well as real-time electricity consumption monitoring, to create a more effective system for carbon emission tracking and electricity consumption management, based on power big data. These two methods are more effective in monitoring CO 2 emissions and power consumption management with effective percentages. By combining these two methods, we possibly achieve a notable mark in the CO 2 monitoring tasks, which is the acceptable truth. The proposed study is assessed based on the Guanxi metal smelting industry, considering the evaluation location for the above two approaches, with separate strengths. So, we also evaluate the efficacy of the proposed plan in the exact location to prove its efficacy. The experiment was conducted under 2020 CO 2 records, and the total CO 2 emission from this sector was reported at 58.47 million tons, a 42.79% increase compared with 2014. Based on this, we implement the proposed novel hybrid approach and start performing the real-time emission tracking tasks of 75 metal smelting enterprises using electricity big data. The experiment result shows that the proposed hybrid approach improved effectively in tracking CO 2 emissions compared with the individual achievement of the above fusion methods. This approach tracks up to 92% of real-time CO 2 emissions, 20% higher than the existing individual strengths. This improvement was achieved by using the ability of electricity consumption coefficients ranging from 10 g CO 2 /kWh to 1200 g CO 2 /kWh to capture the direct and indirect emissions from electricity consumption effectively. Similarly, we obtained better results in monitoring electricity consumption, which is about 85% of total electricity use. Overall, the suggested hybrid fusion model is essential to tracking CO 2 carbon emissions and is a basic model that balances both emission and consumption outcomes more precisely.