Real-Time Carbon Emission Monitoring and Accounting System Based on the Internet of Things (IoT)

A real-time monitoring and accounting system for carbon emissions based on Internet of Things is proposed in this paper based on dynamic time series analysis and machine learning. The proposed algorithm can forecast carbon emissions precisely by using time series analysis and machine learning technology. It can adjust the forecast model dynamically and improve the precision of forecast. Compared with traditional carbon emission accounting methods based on fixed formulas or simple regression models, the proposed method not only has better forecasting precision, but also more flexible and adaptable. To verify the validity of the proposed algorithm, we choose a number of different monitoring scenarios, and validate the algorithm with a simulation platform. The experiment results indicate that the error of this method is about 15 % less than that of the proposed method. Compared with the conventional regression model and the linear time series model, the forecast precision is improved obviously, and the real time response ability is better. Moreover, the system can adjust the forecast parameters based on the data collected in real time, so that the precision and stability of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> monitoring can be improved.