Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans
Engineering Applications of Artificial Intelligence
; 123, 2023.
Article
in English
| Scopus | ID: covidwho-2312827
ABSTRACT
Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examines the impact of the coronavirus on power usage in two US states from January 2020 to December 2020. A wide range of machine learning (ML) algorithms and ensemble learning are employed to conduct the analysis. The findings showed a surprising increase in monthly power use changes in Florida and Texas during the COVID-19 pandemic, in contrast to New York, where usage decreased over the same period. In Texas, the quantity of power usage rises from 2% to 6% practically every month, except for September, when it decreased by around 1%. For Florida, except for May, which showed a fall of roughly 2.5%, the growth varied from 2.5% to 7.5%. This indicates the need for more extensive research into such systems and the applicability of adopting groups of algorithms in learning the trends of electric power demand during uncertain events. Such learning will be helpful in forecasting future power demand changes due to especially public health-related scenarios. © 2023 Elsevier Ltd
City-scale electricity usage; COVID-19; Energy analysis; Forecasting; Machine learning; Economics; Electric loads; Electric power utilization; Learning algorithms; Uncertainty analysis; City scale; Electricity usage; Ensemble learning; Florida; Machine-learning; Power; Power usage; Uncertainty management
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Case report
Language:
English
Journal:
Engineering Applications of Artificial Intelligence
Year:
2023
Document Type:
Article
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