Comparative Analysis of Machine Learning Models for Predicting Electric Vehicle Range - Airlangga, Gregorius (STIKOM Tunas Bangsa Pematangsiantar, 2024)
This research presents a comprehensive analysis of various machine learning models to predict the electric range of electric vehicles (EVs). In the context of growing environmental concerns and the push for sustainable transportation, accurate prediction of EV range is crucial for consumer trust and wider adoption. We evaluated five different models: Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regressor, and Gradient Boosting Regressor, using a dataset that included a diverse array of EV attributes. The primary evaluation metric was the Mean Squared Error (MSE), applied both in cross-validation and on a test set. Our findings revealed significant differences in performance between linear models and ensemble methods. Linear models, while computationally efficient and interpretable, showed modest predictive capabilities, likely limited by their inability to capture complex, non-linear relationships in the data. Notably, Lasso Regression exhibited the highest error rates, possibly due to its feature exclusion in regularization. In contrast, ensemble methods, particularly the Random Forest Regressor and Gradient Boosting Regressor, demonstrated superior performance, effectively modeling non-linear relationships and intricate feature interactions. This study underscores the importance of model selection in predictive tasks, highlighting that more complex models, such as ensemble methods, are often more suitable for datasets with multifaceted interactions and non-linearities. The results of this research contribute to the evolving field of electric vehicle technology, providing insights that can guide future developments in EV range prediction, a key factor in the advancement of sustainable transportation. This research aids in understanding the application of machine learning in EV range prediction and lays the groundwork for future exploration, potentially incorporating real-time data and external factors for enhanced accuracy.