Improving load forecast in energy markets during COVID-19

Ziyun Wang, Hao Wang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 2021 - ACM International Conference on Systems for Energy-Efficient Built Environments
EditorsOmprakash Gnawali, Zoltan Nagy
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)9781450391146
Publication statusPublished - 2021
EventACM Conference on Embedded Systems for Energy-Efficient Buildings 2021 - Coimbra, Portugal
Duration: 17 Nov 202118 Nov 2021
Conference number: 8th


ConferenceACM Conference on Embedded Systems for Energy-Efficient Buildings 2021
Abbreviated titleBuildSys’21
Internet address

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