# Improving Load Forecast in Energy Markets During COVID-19

arXiv: https://arxiv.org/abs/2110.00181 · HTML: https://mlsanity.com/paper/2110.00181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector](https://mlsanity.com/paper/2005.06631/) (2020)
- [Using Mobility for Electrical Load Forecasting During the COVID-19 Pandemic](https://mlsanity.com/paper/2006.08826/) (2020)

## Built on by (most cited first, 1 of 1)

- [FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildings](https://mlsanity.com/paper/2209.01338/) (2022)

## Abstract (arXiv)

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.