  The abrupt outbreak of the COVID-19 pandemic was the most significant event
in 2020, which had profound and lasting impacts across the world. Studies on
energy markets observed a decline in energy demand and changes in energy
consumption behaviors during COVID-19. However, as an essential part of system
operation, how the load forecasting performs amid COVID-19 is not well
understood. This paper aims to bridge the research gap by systematically
evaluating models and features that can be used to improve the load forecasting
performance amid COVID-19. Using real-world data from the New York Independent
System Operator, our analysis employs three deep learning models and adopts
both novel COVID-related features as well as classical weather-related
features. We also propose simulating the stay-at-home situation with
pre-stay-at-home weekend data and demonstrate its effectiveness in improving
load forecasting accuracy during COVID-19.
