Impact Analysis of COVID-19 in Bangladesh Power Sector and Recommendations based on Practical Data and Machine Learning Approach

摘要

This paper investigates the impact of COVID-19 on the power sector in Bangladesh, how the country has dealt with it, and explores the path to stability. The study employs data visualisation and complex statistics to examine critical data about power systems in Bangladesh. This includes load patterns on a daily, monthly, annual, weekend, and weekday basis. Significant alterations in these patterns have been observed during our study e.g., in April and May of 2020, the power demand decreased by approximately 15.4% and 17.2%, respectively, compared to the corresponding period in 2019. We have used a Long-Short-Term Memory (LSTM) framework to predict the load profile of 2020 excluding COVID-19 effects. This model is compared with the actual load profile to determine the degree to which COVID-19 has impacted. The comparison indicates that the average power demand decreased by approximately 19.5% in April 2020 and 18.3% in May 2020, relative to its projected value. The study also investigates system stability by analyzing transmission loss and load factor, and the environmental effect by analyzing the Carbon Dioxide emission rate. Finally, the study provides recommendations for overcoming future disasters, such as developing more resilient power systems, investing in renewable energy, and improving energy efficiency.