Improving Load Forecast in Energy Markets During COVID-19

• URL: http://arxiv.org/abs/2110.00181v1
• Date: Fri, 1 Oct 2021 02:55:06 GMT
• Title: Improving Load Forecast in Energy Markets During COVID-19
• Authors: Ziyun Wang and Hao Wang
• Abstract summary: The abrupt outbreak of the COVID-19 pandemic was the most significant event in 2020, which had profound and lasting impacts across the world. 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.
• Score: 5.128521783181427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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