Impact of Covid-19 and SLP-SVR Algorithms on Short-Term Load Forecast, Case Study: EVNHCMC

Electricity demand forecasting has gotten much attention from power producers and regional researchers over the years. It is not easy to find the right forecasting approaches because there are so many variables to consider, such as temperature, humidity, wind, demographics, seasons of the year, days of the week, holidays, etc. The pandemic of COVID-19 has had a direct effect on electricity demand. When considering COVID-19 in addition to the common impact factors listed above, the paper examines the impact of COVID-19 on load components and irregular changes in power consumption demand. The paper then proposes a framework for short-term load forecasting based on a combination of the Standardized Load Profile (SLP) and the Support Vector Regression (SVR) machine learning algorithm, taking into account COVID-19.