Volume 23: Sustainable Energy Solutions for a Post-COVID Recovery towards a Better Future: Part VI

Statistical Analysis of Renewable and Non-Renewable Energy Consumption under the Epidemic Situation Xiaomin Wang, Yuan Pu, Bo Qiu

Abstract

Energy consumption not only reflects the level of energy utilizing technology, but also reflects the level of economic development. Since the sudden outbreak of COVID-19 in 2020, world energy consumption dropped drastically. Therefore, the effective energy management is particularly important. In this paper, long short-term memory (LSTM) time series prediction model is used to conduct statistical analysis on the consumption of coal, petroleum, natural gas and renewable energy in the United States. The goal is to analyze the impact of the epidemic based on the consumption statistics from different energy types and predict the trend of energy consumption in the United States. Based on the historical data and LSTM model, it can be concluded that coal and natural gas consumption have obvious jump in the epidemic, while petroleum and renewable energy consumption are relatively stable.

Keywords energy consumption statistics, energy consumption prediction, LSTM prediction model, energy management