Time Series Modeling and Forecasting Analysis of Energy Consumption in a Rare Earth Workshop

Abstract

Under the framework of the dual-carbon strategy, an in-depth study of carbon emission efficiency in energy consumption is of strategic importance for China to achieve its goals of carbon peaking and carbon neutrality, as well as to guide the economic and social transition towards a green, low-carbon, and sustainable development model. This study aims to provide empirical support in this field through detailed data analysis and predictive modeling. The paper employs the ARIMA time series prediction model, focusing on analyzing the power consumption data of a sintered NdFeB workshop from 2023 to 2024 to make accurate predictions of future energy use and carbon emission trends. The research process is rigorous: first, unit root tests (ADF tests) and white noise tests are conducted on the collected dataset to ensure data stationarity and reliability. Next, the Bayesian Information Criterion (BIC) is used to select optimal model parameters, enhancing the accuracy and efficiency of predictions. In terms of data processing, the dataset is divided into a training set and a test set in an 8:2 ratio, ensuring the model learns data characteristics thoroughly during training and demonstrates strong generalization in testing. The experimental results indicate that applying the ARIMA model to forecast power consumption data in the workshop effectively reveals intrinsic energy consumption patterns and provides valuable guidance for optimizing workshop energy management and reducing carbon emissions.