A novel neuro-probabilistic framework for energy demand forecasting in Electric Vehicle integration

M.A. Rojo-Yepes, C.D. Zuluaga-Ríos, S.D. Saldarriaga-Zuluaga, J.M. López-Lezama, N. Muñoz-Galeano

World Electric Vehicle Journal Vol. 15, nº. 11, pp. 493-1 - 493-18

Summary:

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.

Spanish layman's summary:

Este paper presenta un modelo de  grid-to-vehicle que utiliza métodos probabilísticos y redes neuronales para predecir la demanda de carga de vehículos eléctricos en Medellín, Colombia. Adaptado a datos locales, el modelo optimiza la infraestructura de carga y las operaciones de la red. Los resultados apoyan la planificación, inversión y políticas para una integración sostenible de vehículos eléctricos.

English layman's summary:

This paper presents a grid-to-vehicle model using probabilistic methods and neural networks to predict EV charging demand in Medellin, Colombia. Tailored to local data, it optimizes EV charging infrastructure and grid operations. Results support planning, investment, and policy for sustainable EV integration.

Keywords: electric vehicle charging; forecasting; neural networks; probabilistic approach

JCR Impact Factor and WoS quartile: 2,600 - Q2 (2023)

DOI reference: https://doi.org/10.3390/wevj15110493

Published on paper: November 2024.

Published on-line: October 2024.

Citation:
M.A. Rojo-Yepes, C.D. Zuluaga-Ríos, S.D. Saldarriaga-Zuluaga, J.M. López-Lezama, N. Muñoz-Galeano, A novel neuro-probabilistic framework for energy demand forecasting in Electric Vehicle integration. World Electric Vehicle Journal. Vol. 15, nº. 11, pp. 493-1 - 493-18, November 2024. [Online: October 2024]