Improving load forecast in energy markets during COVID-19
8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021
; : 168-171, 2021.
Article
in English
| Scopus | ID: covidwho-1599143
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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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. © 2021 ACM.
COVID-19; deep, neural, network; energy, demand; energy, market; load, forecasting; time, series; Electric, power, plant, loads; Energy, management; Energy, utilization; Forecasting; Power, markets; Energy, changes; Energy, demands; Energy, markets; Energy-consumption; Load, forecast; Research, gaps; Systems, operation; Times, series; Deep, neural, networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021
Year:
2021
Document Type:
Article
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