Improving Load Forecast in Energy Markets During COVID-19
This work addresses the challenge of accurate load forecasting for energy system operators during the COVID-19 pandemic, which is incremental as it applies existing methods to new data with novel features.
The paper tackled the problem of load forecasting in energy markets during COVID-19 by systematically evaluating models and features, resulting in improved accuracy through the use of deep learning models, COVID-related features, and a simulation of stay-at-home situations.
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.