Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends
This study presents a comprehensive forecasting approach to evaluate the future of electric vehicle (EV) adoption in the United Kingdom through 2035. Using three complementary models—SARIMAX, Prophet with regressors, and XGBoost—the analysis balances statistical robustness, policy sensitivity, and i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/7/430 |
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| author | Shima Veysi Mohammad Moshfeghi Amir Sadrfaridpour Peiman Emamy |
| author_facet | Shima Veysi Mohammad Moshfeghi Amir Sadrfaridpour Peiman Emamy |
| author_sort | Shima Veysi |
| collection | DOAJ |
| description | This study presents a comprehensive forecasting approach to evaluate the future of electric vehicle (EV) adoption in the United Kingdom through 2035. Using three complementary models—SARIMAX, Prophet with regressors, and XGBoost—the analysis balances statistical robustness, policy sensitivity, and interpretability. Historical data from 2015 to 2024 was used to train the models, incorporating key drivers such as battery prices, GDP growth, public charging infrastructure, and government policy targets. XGBoost demonstrated the highest historical accuracy, making it a strong explanatory tool, particularly for assessing variable importance. However, due to its limitations in extrapolation, it was not used for long-term forecasting. Instead, Prophet and SARIMAX were employed to project EV sales under baseline, optimistic, and pessimistic policy scenarios. The results suggest that the UK could achieve between 2,964,000 and 3,188,000 EV sales by 2035 under baseline assumptions. Scenario analysis revealed high sensitivity to infrastructure growth and policy enforcement, with potential shortfalls of up to 500,000 vehicles in pessimistic scenarios. These findings highlight the importance of sustained government commitment and investment in EV infrastructure and supply chains. By combining machine learning diagnostics with transparent forecasting models, the study offers actionable insights for policymakers, investors, and stakeholders navigating the UK’s zero-emission transition. |
| format | Article |
| id | doaj-art-52aea8e9ebfa4069ab8582337d913f23 |
| institution | Matheson Library |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-52aea8e9ebfa4069ab8582337d913f232025-07-25T13:10:36ZengMDPI AGAlgorithms1999-48932025-07-0118743010.3390/a18070430Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global TrendsShima Veysi0Mohammad Moshfeghi1Amir Sadrfaridpour2Peiman Emamy3Center for Factories of the Future (C4FF), Kenilworth CV8 1EB, UKDepartment of Mechanical Engineering, Sogang University, Seoul 04066, Republic of KoreaIndependent Researcher, Solihull B90 4PE, UKIrsa San’at Shahr-no Afarin Engineering Company (Ltd.), Qazvin 34149-63851, IranThis study presents a comprehensive forecasting approach to evaluate the future of electric vehicle (EV) adoption in the United Kingdom through 2035. Using three complementary models—SARIMAX, Prophet with regressors, and XGBoost—the analysis balances statistical robustness, policy sensitivity, and interpretability. Historical data from 2015 to 2024 was used to train the models, incorporating key drivers such as battery prices, GDP growth, public charging infrastructure, and government policy targets. XGBoost demonstrated the highest historical accuracy, making it a strong explanatory tool, particularly for assessing variable importance. However, due to its limitations in extrapolation, it was not used for long-term forecasting. Instead, Prophet and SARIMAX were employed to project EV sales under baseline, optimistic, and pessimistic policy scenarios. The results suggest that the UK could achieve between 2,964,000 and 3,188,000 EV sales by 2035 under baseline assumptions. Scenario analysis revealed high sensitivity to infrastructure growth and policy enforcement, with potential shortfalls of up to 500,000 vehicles in pessimistic scenarios. These findings highlight the importance of sustained government commitment and investment in EV infrastructure and supply chains. By combining machine learning diagnostics with transparent forecasting models, the study offers actionable insights for policymakers, investors, and stakeholders navigating the UK’s zero-emission transition.https://www.mdpi.com/1999-4893/18/7/430electric vehiclesales forecastmachine learningtime series modelsglobal trendsUK market |
| spellingShingle | Shima Veysi Mohammad Moshfeghi Amir Sadrfaridpour Peiman Emamy Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends Algorithms electric vehicle sales forecast machine learning time series models global trends UK market |
| title | Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends |
| title_full | Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends |
| title_fullStr | Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends |
| title_full_unstemmed | Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends |
| title_short | Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends |
| title_sort | electric vehicle sales forecast for the uk integrating machine learning time series models and global trends |
| topic | electric vehicle sales forecast machine learning time series models global trends UK market |
| url | https://www.mdpi.com/1999-4893/18/7/430 |
| work_keys_str_mv | AT shimaveysi electricvehiclesalesforecastfortheukintegratingmachinelearningtimeseriesmodelsandglobaltrends AT mohammadmoshfeghi electricvehiclesalesforecastfortheukintegratingmachinelearningtimeseriesmodelsandglobaltrends AT amirsadrfaridpour electricvehiclesalesforecastfortheukintegratingmachinelearningtimeseriesmodelsandglobaltrends AT peimanemamy electricvehiclesalesforecastfortheukintegratingmachinelearningtimeseriesmodelsandglobaltrends |