Prediction of Electric Power Generation for Green Mobility Vehicles

With the increasing adoption of electric motor-cycles in urban environments, efficient energy management becomes essential to maximize the autonomy and sustainability of these vehicles. This study proposes the development of forecasting models to predict energy consumption and generation as means to optimize the charging of electric motorcycle batteries. Three models are explored in this work, namely multiple linear regression, LSTM (Long Short-Term Memory) neural networks, and XGBoost (Extreme Gradient Boosting). The performance of each model is assessed through various metrics. The results indicate that the LSTM model exhibited the best performance, particularly in identifying complex temporal patterns in solar radiation data. However, XGBoost also proved to be reasonable, while multiple linear regression was less satisfactory. The study discusses its limitations, such as the lack of deep refinement of model parameters, and future perspectives, including the exploration of other models and the implementation of strategies for predictive battery charging management.