Short-Term Load Forecasting Model Considering the Impact of COVID-19 Lockdown in Bangladesh

Abstract

The impact of COVID-19 lockdown on short-term load forecasting in Bangladesh has been investigated in this paper. Machine learning models have been proved to be the most efficient regarding such prediction. Models like Artificial Neural Network (ANN), Long Short-Term Memory (LSTM) and Random Forest (RF) have been used in this study to build robust models taking the COVID-19 lockdown situation into account. Data sets for the models were formulated by taking daily generation reports, weather indicators and holidays. This study aims to compare different machine learning models to find out the best model for load forecasting keeping into account the impact of COVID-19 lockdown. The results of these methods have been compared based on accuracy metrics. It has been observed that LSTM shows the least error among the compared models.