A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration
This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/11/493 |
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| author | Miguel Ángel Rojo-Yepes Carlos D. Zuluaga-Ríos Sergio D. Saldarriaga-Zuluaga Jesús M. López-Lezama Nicolas Muñoz-Galeano |
| author_facet | Miguel Ángel Rojo-Yepes Carlos D. Zuluaga-Ríos Sergio D. Saldarriaga-Zuluaga Jesús M. López-Lezama Nicolas Muñoz-Galeano |
| author_sort | Miguel Ángel Rojo-Yepes |
| collection | DOAJ |
| description | This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid. |
| format | Article |
| id | doaj-art-2581c2ca74554e55b4b0bcb9b5727944 |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-2581c2ca74554e55b4b0bcb9b57279442025-08-20T02:04:43ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-10-01151149310.3390/wevj15110493A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle IntegrationMiguel Ángel Rojo-Yepes0Carlos D. Zuluaga-Ríos1Sergio D. Saldarriaga-Zuluaga2Jesús M. López-Lezama3Nicolas Muñoz-Galeano4Departamento de Eléctrica, Facultad de Ingenieria, Institución Universitaria Pascual Bravo, Calle 73 No. 73A-226, Medellin 050036, ColombiaInstituto de Investigación Tecnológica, Universidad Pontificia Comillas, Calle de Alberto Aguilera, 23, 280015 Madrid, SpainDepartamento de Eléctrica, Facultad de Ingenieria, Institución Universitaria Pascual Bravo, Calle 73 No. 73A-226, Medellin 050036, ColombiaGrupo de Investigación en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellin 050010, ColombiaGrupo de Investigación en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellin 050010, ColombiaThis paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.https://www.mdpi.com/2032-6653/15/11/493electric vehicle chargingforecastingneural networksprobabilistic approach |
| spellingShingle | Miguel Ángel Rojo-Yepes Carlos D. Zuluaga-Ríos Sergio D. Saldarriaga-Zuluaga Jesús M. López-Lezama Nicolas Muñoz-Galeano A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration World Electric Vehicle Journal electric vehicle charging forecasting neural networks probabilistic approach |
| title | A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration |
| title_full | A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration |
| title_fullStr | A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration |
| title_full_unstemmed | A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration |
| title_short | A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration |
| title_sort | novel neuro probabilistic framework for energy demand forecasting in electric vehicle integration |
| topic | electric vehicle charging forecasting neural networks probabilistic approach |
| url | https://www.mdpi.com/2032-6653/15/11/493 |
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