Título: Planejamento Otimizado de Eletropostos: Uma Abordagem Híbrida com Previsão de Demanda e Otimização Evolucionária para Belo Horizonte
Autores: Guilherme Luis Soares Santos, Bruno Gomes & Carlos Alexandre Silva
Resumo: The rapid growth of the electric vehicle (EV) fleet in Belo Horizonte calls for robust planning of charging infrastructure. This study addresses this challenge by proposing a methodology that integrates demand forecasting models — Polynomial Regression and ARIMA — with a Genetic Algorithm for geospatial optimization of charging stations. Predictive analysis revealed the ARIMA model’s superiority for long-term projections. The resulting geospatial optimization not only meets the projected demand but also maximizes economic viability by prioritizing higher-income regions. The proposed solution recommends installing a 15% surplus of charging stations over the minimum demand, demonstrating that this strategy is economically advantageous and offers valuable insights for smart and sustainable urban planning.
Palavras-chave: Genetic Algorithms; Demand Forecasting; Geospatial Optimization; ARIMA; Polynomial Regression; Electric Vehicle Charging Stations.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1191292
Artigo em PDF: CBIC_2025_paper1191292.pdf
Arquivo BibTeX:
CBIC_2025_1191292.bib