Impact of the COVID-19 lockdowns on electricity and natural gas consumption in the different industrial zones and forecasting consumption amounts: Turkey case study

The COVID-19 lockdowns have adversely affected the national economies and caused fluctuations in the energy industry. This study examined how the lockdowns during the COVID-19 pandemic affected the amount of electricity and natural gas consumption in four organized industrial zones in the Turkey. A significant decrease was observed in electricity and natural gas consumption amounts in April and May when lockdowns were also applied in four industrial zones. In April, electricity consumption decreased between 72 and 43%, and natural gas consumption decreased between 77 and 57%. In May, electricity consumption decreased between 60 and 32%, and natural gas consumption decreased between 69 and 45%. These decreases in industrial zones show that the economy has been significantly affected. Furthermore, in this study, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters models were developed to predict electricity and natural gas consumption of an industrial zone. ARIMA(0,0,2)(2,1,0) 7 and ARIMA(0,0,2)(0,1,1) 7 models were chosen as the best model for the electricity and natural gas consumption data respectively with a minimum MAPE(Electricity) was 1.37%, RMSEElectricity was 87.2, R-Electricity(2) was 0.99, MAPE(Gas) was 5.42% and RMSEGas was 50.9, R-Gas(2) was 0.92. Electricity and natural gas consumption was forecasted for the next ten days (10-19 March 2021) according to ARIMA models with 80% and 95% confidence intervals. In addition, in this study, the impact of low energy usage in the industrial zone due to the COVID-19 lockdowns on model prediction performance was also examined. The obtained results showed that the COVID-19 lockdowns were reduced the ARIMA model prediction accuracy.

Automated summary

The COVID-19 lockdowns significantly decreased electricity and natural gas consumption in industrial zones in Turkey. ARIMA and Holt-Winters models were used to predict consumption levels, showing a strong impact of the lockdowns on energy usage. Despite accurate predictions, the models were affected by the reduced energy usage during lockdowns.