Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models
Accurately modeling and forecasting electricity consumption remains a challenging task due to the large number of the statistical properties that characterize this time series such as seasonality, trend, sudden changes, slow decay of autocorrelation function, among many others. This study contribute...
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2023
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| Online Access: | http://dx.doi.org/10.1016/j.scs.2023.104860 https://www.sciencedirect.com/science/article/pii/S2210670723004717 http://hdl.handle.net/10576/47967 |
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| _version_ | 1857415083857018880 |
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| author | Lanouar, Charfeddine |
| author2 | Zaidan, Esmat Alban, Ahmad Qadeib Bennasr, Hamdi Abulibdeh, Ammar |
| author2_role | author author author author |
| author_facet | Lanouar, Charfeddine Zaidan, Esmat Alban, Ahmad Qadeib Bennasr, Hamdi Abulibdeh, Ammar |
| author_role | author |
| dc.creator.none.fl_str_mv | Lanouar, Charfeddine Zaidan, Esmat Alban, Ahmad Qadeib Bennasr, Hamdi Abulibdeh, Ammar |
| dc.date.none.fl_str_mv | 2023-09-26T07:13:03Z 2023-11-30 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.scs.2023.104860 22106707 https://www.sciencedirect.com/science/article/pii/S2210670723004717 http://hdl.handle.net/10576/47967 98 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Electricity consumption Forecasting COVID-19 Nonlinear econometric models Machine and deep learning models |
| dc.title.none.fl_str_mv | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Accurately modeling and forecasting electricity consumption remains a challenging task due to the large number of the statistical properties that characterize this time series such as seasonality, trend, sudden changes, slow decay of autocorrelation function, among many others. This study contributes to this literature by using and comparing four advanced time series econometrics models, and four machine learning and deep learning models11These models include the autoregressive model with seasonality, autoregressive models with exogenous variables, the autoregressive fractionally integrated moving average model with exogenous variables, the three state autoregressive Markov switching model with exogenous variable, Prophet, EXtreme Gradient Boosting, Long-Short-Term Memory and Support Vector Regression. to analyze and forecast electricity consumption during COVID-19 pre-lockdown, lockdown, releasing-lockdown, and post-lockdown phases. Monthly data on Qatar’s total electricity consumption has been used from January 2010 to December 2021. The empirical findings demonstrate that both econometric and machine learning models are able to capture most of the important statistical features characterizing electricity consumption. In particular, it is found that climate change based factors, e.g temperature, rainfall, mean sea-level pressure and wind speed, are key determinants of electricity consumption. In terms of forecasting, the results indicate that the autoregressive fractionally integrated moving average and the three state autoregressive Markov switching models with exogenous variables outperform all other models. Policy implications and energy-environmental recommendations are proposed and discussed. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_5e5cd452e52b5a74fac255da765a92ff |
| identifier_str_mv | 22106707 98 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/47967 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series modelsLanouar, CharfeddineZaidan, EsmatAlban, Ahmad QadeibBennasr, HamdiAbulibdeh, AmmarElectricity consumptionForecastingCOVID-19Nonlinear econometric modelsMachine and deep learning modelsAccurately modeling and forecasting electricity consumption remains a challenging task due to the large number of the statistical properties that characterize this time series such as seasonality, trend, sudden changes, slow decay of autocorrelation function, among many others. This study contributes to this literature by using and comparing four advanced time series econometrics models, and four machine learning and deep learning models11These models include the autoregressive model with seasonality, autoregressive models with exogenous variables, the autoregressive fractionally integrated moving average model with exogenous variables, the three state autoregressive Markov switching model with exogenous variable, Prophet, EXtreme Gradient Boosting, Long-Short-Term Memory and Support Vector Regression. to analyze and forecast electricity consumption during COVID-19 pre-lockdown, lockdown, releasing-lockdown, and post-lockdown phases. Monthly data on Qatar’s total electricity consumption has been used from January 2010 to December 2021. The empirical findings demonstrate that both econometric and machine learning models are able to capture most of the important statistical features characterizing electricity consumption. In particular, it is found that climate change based factors, e.g temperature, rainfall, mean sea-level pressure and wind speed, are key determinants of electricity consumption. In terms of forecasting, the results indicate that the autoregressive fractionally integrated moving average and the three state autoregressive Markov switching models with exogenous variables outperform all other models. Policy implications and energy-environmental recommendations are proposed and discussed.This publication was made possible by an NPRP award [NPRP13S0206-200272] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The open access publication of this article was funded by the Qatar National Library (QNL).Elsevier2023-09-26T07:13:03Z2023-11-30Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.scs.2023.10486022106707https://www.sciencedirect.com/science/article/pii/S2210670723004717http://hdl.handle.net/10576/4796798enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/479672024-07-23T10:58:26Z |
| spellingShingle | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models Lanouar, Charfeddine Electricity consumption Forecasting COVID-19 Nonlinear econometric models Machine and deep learning models |
| status_str | publishedVersion |
| title | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models |
| title_full | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models |
| title_fullStr | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models |
| title_full_unstemmed | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models |
| title_short | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models |
| title_sort | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models |
| topic | Electricity consumption Forecasting COVID-19 Nonlinear econometric models Machine and deep learning models |
| url | http://dx.doi.org/10.1016/j.scs.2023.104860 https://www.sciencedirect.com/science/article/pii/S2210670723004717 http://hdl.handle.net/10576/47967 |