Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland
Reducing greenhouse gas (GHG) emissions from the transport sector is central to achieving Ireland’s national climate goals. To support the Climate Action Plan target of registering 945,000 electric vehicles (EVs) by 2030, this study develops a hybrid time series forecasting framework that...
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IEEE
2025-01-01
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| author | Afaq Khattak Brian Caulfield |
| author_facet | Afaq Khattak Brian Caulfield |
| author_sort | Afaq Khattak |
| collection | DOAJ |
| description | Reducing greenhouse gas (GHG) emissions from the transport sector is central to achieving Ireland’s national climate goals. To support the Climate Action Plan target of registering 945,000 electric vehicles (EVs) by 2030, this study develops a hybrid time series forecasting framework that combines a Seasonal Autoregressive Integrated Moving Average (SARIMA) model with a Bayesian Optimized Long Short-Term Memory (BO-LSTM) network. SARIMA captures linear and seasonal patterns in monthly EV registration data, while BO-LSTM models the non-linear residual structure. Monthly data from January 2010 to October 2024, sourced from the Society of the Irish Motor Industry (SIMI), is used for model training and evaluation. The SARIMA-BO–LSTM model achieves a Mean Absolute Error (MAE) of 742.99, Root Mean Squared Error (RMSE) of 1200, and R2 of 0.93, outperforming several baseline statistical and machine learning models. Scenario-based forecasts are conducted under three conditions: Business-as-Usual, Accelerated Adoption, and Saturation Bound. Projections show that under the Business-as-Usual scenario, Ireland is likely to fall short of the 2030 EV target. In contrast, the Accelerated Adoption scenario meets the target through sustained exponential growth backed by strong policy and infrastructure investment. The Saturation Bound scenario also reaches the target, though with slower growth after an initial surge due to behavioral and economic constraints. The proposed forecasting framework provides a basis for planning infrastructure, incentives, and regulatory measures consistent with Ireland’s decarbonization objectives. |
| format | Article |
| id | doaj-art-596d4b105e4048c0975eff3d79117400 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-596d4b105e4048c0975eff3d791174002025-08-20T02:35:47ZengIEEEIEEE Access2169-35362025-01-011310270610272310.1109/ACCESS.2025.357932111031436Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in IrelandAfaq Khattak0https://orcid.org/0000-0002-5623-7897Brian Caulfield1Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, IrelandDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, IrelandReducing greenhouse gas (GHG) emissions from the transport sector is central to achieving Ireland’s national climate goals. To support the Climate Action Plan target of registering 945,000 electric vehicles (EVs) by 2030, this study develops a hybrid time series forecasting framework that combines a Seasonal Autoregressive Integrated Moving Average (SARIMA) model with a Bayesian Optimized Long Short-Term Memory (BO-LSTM) network. SARIMA captures linear and seasonal patterns in monthly EV registration data, while BO-LSTM models the non-linear residual structure. Monthly data from January 2010 to October 2024, sourced from the Society of the Irish Motor Industry (SIMI), is used for model training and evaluation. The SARIMA-BO–LSTM model achieves a Mean Absolute Error (MAE) of 742.99, Root Mean Squared Error (RMSE) of 1200, and R2 of 0.93, outperforming several baseline statistical and machine learning models. Scenario-based forecasts are conducted under three conditions: Business-as-Usual, Accelerated Adoption, and Saturation Bound. Projections show that under the Business-as-Usual scenario, Ireland is likely to fall short of the 2030 EV target. In contrast, the Accelerated Adoption scenario meets the target through sustained exponential growth backed by strong policy and infrastructure investment. The Saturation Bound scenario also reaches the target, though with slower growth after an initial surge due to behavioral and economic constraints. The proposed forecasting framework provides a basis for planning infrastructure, incentives, and regulatory measures consistent with Ireland’s decarbonization objectives.https://ieeexplore.ieee.org/document/11031436/Electric vehiclesforecastingtime series modelingscenario-based analysis |
| spellingShingle | Afaq Khattak Brian Caulfield Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland IEEE Access Electric vehicles forecasting time series modeling scenario-based analysis |
| title | Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland |
| title_full | Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland |
| title_fullStr | Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland |
| title_full_unstemmed | Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland |
| title_short | Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland |
| title_sort | hybrid sarima bo lstm framework for forecasting ev adoption a road to net zero in ireland |
| topic | Electric vehicles forecasting time series modeling scenario-based analysis |
| url | https://ieeexplore.ieee.org/document/11031436/ |
| work_keys_str_mv | AT afaqkhattak hybridsarimabolstmframeworkforforecastingevadoptionaroadtonetzeroinireland AT briancaulfield hybridsarimabolstmframeworkforforecastingevadoptionaroadtonetzeroinireland |