A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting
Energy Reports
; 9:1887-1895, 2023.
Article
Dans Anglais
| Scopus | ID: covidwho-2178237
ABSTRACT
Electricity demand forecasting is crucial for practical power system management. However, during the COVID-19 pandemic, the electricity demand system deviated from normal system, which has detrimental bias effect in future forecasts. To overcome this problem, we propose a deep learning framework with a COVID-19 adjustment for electricity demand forecasting. More specifically, we first designed COVID-19 related variables and applied a multiple linear regression model. After eliminating the impact of COVID-19, we employed an efficient deep learning algorithm, long short-term memory multiseasonal net deseasonalized approach, to model residuals from the linear model aforementioned. Finally, we demonstrated the merits of the proposed framework using the electricity demand in Taixing, Jiangsu, China, from May 13, 2018 to August 2, 2021. © 2023 The Author(s)
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
Scopus
langue:
Anglais
Revue:
Energy Reports
Année:
2023
Type de document:
Article
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