Overview, modeling and forecasting the effects of COVID-19 pandemic on energy market and electricity demand: a case study on Turkey

In this study, the overall energy status of Turkey in the early stage of the COVID-19 pandemic was analyzed. The situation in the energy market and electricity demand in the pandemic period as global issues were investigated in Turkey as a developing country. In addition to this general overview using limited data for the early period of the pandemic, energy demand and energy generation values were modeled utilizing machine learning approaches. Daily energy demand values were modeled and forecasted by utilizing Nonlinear Autoregression Neural Network (NARNN), Auto-Regressive Integrated Moving Average (ARIMA), and Long-Short Term Memory (LSTM) techniques. According to the results of the first stage of the modeling process, the LSTM approach was found as the most accurate model. In the second step of the modeling and forecasting analysis, monthly electricity generation values from natural gas and coal were predicted. In the energy generation forecasting analysis for April-December 2020 period, contraction in energy generation by natural gas and coal were obtained as 6.27% and 7.19%, respectively in comparison to 2019. Finally, the energy market during and after the pandemic was evaluated and the strategies to be implemented in the post-pandemic process were discussed.