Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models
<p>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 c...
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2023
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| _version_ | 1864513561252855808 |
|---|---|
| author | Lanouar Charfeddine (10705000) |
| author2 | Esmat Zaidan (16855203) Ahmad Qadeib Alban (16855206) Hamdi Bennasr (16855209) Ammar Abulibdeh (15785928) |
| author2_role | author author author author |
| author_facet | Lanouar Charfeddine (10705000) Esmat Zaidan (16855203) Ahmad Qadeib Alban (16855206) Hamdi Bennasr (16855209) Ammar Abulibdeh (15785928) |
| author_role | author |
| dc.creator.none.fl_str_mv | Lanouar Charfeddine (10705000) Esmat Zaidan (16855203) Ahmad Qadeib Alban (16855206) Hamdi Bennasr (16855209) Ammar Abulibdeh (15785928) |
| dc.date.none.fl_str_mv | 2023-11-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.scs.2023.104860 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Modeling_and_forecasting_electricity_consumption_amid_the_COVID-19_pandemic_Machine_learning_vs_nonlinear_econometric_time_series_models/23994657 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Machine learning Electricity consumption Forecasting COVID-19 Nonlinear econometric models Machine and deep learning models CPP |
| 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 | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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 models 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. </p><h2>Other Information</h2><p>Published in: Sustainable Cities and Society<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.scs.2023.104860" target="_blank">https://dx.doi.org/10.1016/j.scs.2023.104860</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0b3da228179ae15bb93bd3450a8c66a1 |
| identifier_str_mv | 10.1016/j.scs.2023.104860 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23994657 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series modelsLanouar Charfeddine (10705000)Esmat Zaidan (16855203)Ahmad Qadeib Alban (16855206)Hamdi Bennasr (16855209)Ammar Abulibdeh (15785928)EngineeringElectrical engineeringInformation and computing sciencesMachine learningElectricity consumptionForecastingCOVID-19Nonlinear econometric modelsMachine and deep learning modelsCPP<p>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 models 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. </p><h2>Other Information</h2><p>Published in: Sustainable Cities and Society<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.scs.2023.104860" target="_blank">https://dx.doi.org/10.1016/j.scs.2023.104860</a></p>2023-11-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.scs.2023.104860https://figshare.com/articles/journal_contribution/Modeling_and_forecasting_electricity_consumption_amid_the_COVID-19_pandemic_Machine_learning_vs_nonlinear_econometric_time_series_models/23994657CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/239946572023-11-01T00:00:00Z |
| spellingShingle | Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models Lanouar Charfeddine (10705000) Engineering Electrical engineering Information and computing sciences Machine learning Electricity consumption Forecasting COVID-19 Nonlinear econometric models Machine and deep learning models CPP |
| 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 | Engineering Electrical engineering Information and computing sciences Machine learning Electricity consumption Forecasting COVID-19 Nonlinear econometric models Machine and deep learning models CPP |