Research on the calculation model of energy consumption and carbon emissions in the service industry based on time series prediction model

As an important component of the modern economy, the service industry’s energy consumption and carbon emissions increasingly impact the environment. This article constructs an application system for a large amount of electricity data analysis, aiming to accurately calculate and predict energy consumption and carbon emissions in the service industry through a calculation model of electricity carbon. This article combines big data, machine learning, and optimization algorithms. It compares the use of models such as ARIMAX, BP neural network, and LSTM to improve the accuracy and reliability of energy consumption and carbon emission prediction. Through actual data and case verification, the calculation model of electricity-carbon has shown good applicability and prediction accuracy in different scenarios.