Abstract

The existing measurement of the impact of the COVID-19 pandemic on energy consumption is based on changes between the years, which demonstrates the changes in energy consumption over the years without fully reflecting the impact of the pandemic on energy consumption. To better uncover the impact of the COVID-19 pandemic on energy consumption, this research compared pandemic-free scenarios with actual (with COVID-19) energy consumption in 2020, rather than comparing energy consumption between 2020 and 2019 in the existing studies. The simulation approach used for scenario simulation was developed by combing the autoregressive integrated moving average (ARIMA) and back propagation neural network (BP). In the proposed ARIMAR-BP approach, BP was used to correct the error of ARMIA simulation, so as to reduce the error of simulation. The results of the model testing indicate that the simulation error of the developed approach is much lower than that of the BP or ARIMA simulation. The proposed simulation approach was run based on China's electricity consumption from 2015 to 2019 to produce the simulated value of China's electricity consumption from January to August of 2020 in the pandemic-free scenario. The actual electricity consumption was on average 29% lower than the electricity consumption in the pandemic-free scenario. which is much larger than the decline rate derived from year-to-year comparison. In addition, the results of the correlation analysis show the simulated decline in electricity consumption is only positively correlated with the number of new cases of COVID-19 in January–March, when the COVID-19 outbreak in China. This research provides a novel research structure for a more comprehensive understanding of the impact of the pandemic on energy consumption.

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