Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models-Reference-Cited by-同舟云学术

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Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models

Published:2023-11 Issue: Volume:98 Page:104860
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Charfeddine Lanouar,Zaidan Esmat,Alban Ahmad Qadeib,Bennasr Hamdi,Abulibdeh Ammar

Funder

Qatar National Research Fund

Qatar National Library

Qatar Foundation

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference95 articles.

1. Modeling electricity consumption patterns during the covid-19 pandemic across six socioeconomic sectors in the state of qatar;Abulibdeh;Energy Strategy Reviews,2021

2. Spatiotemporal analysis of water-electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors in Doha city, Qatar;Abulibdeh;Applied Energy,2021

3. Time series analysis of environmental quality in the state of Qatar;Abulibdeh;Energy Policy,2022

4. The impact of covid-19 pandemic on electricity consumption and electricity demand forecasting accuracy: empirical evidence from the state of qatar;Abulibdeh;Energy Strategy Reviews,2022

5. Spatial and temporal changes in electricity demand regulatory during pandemic periods: The case of COVID-19 in doha, Qatar;Al-Awadhi;Energy Strategy Reviews,2022

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1. Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis;Sustainable Cities and Society;2024-10

2. Forecasting and unveiling the impeded factors of total export of Bangladesh using nonlinear autoregressive distributed lag and machine learning algorithms;Heliyon;2024-09

3. An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model;Acta Tropica;2024-09

4. Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load;Energy;2024-09

5. Robust-mv-M-LSTM-CI: Robust Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic;Sustainability;2024-08-05

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