Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

Abstract

Covid-19 was a huge catastrophe for the whole world, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and Covid-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the machine learning models LightGBM, NNAT and LSTM worked better in the unstable energy series, while the statistical model ARIMA still had an advantage in the stable energy time series. Overall, the machine learning models outperformed the traditional models in Covid-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during Covid-19.