A Data-Driven Temporal Charge Profiling of Electric Vehicles

Electric vehicles (EVs) are gaining popularity as an efficient and environment-friendly transportation alternative. However, as the number of EVs on the road increases, the demand for electric power to charge them also rises, escalating challenges for the power industry. This requires accurate profiling of EV charging patterns to make informed decisions in future integrated energy systems. To address this problem, various traditional algorithms have been used to forecast the electricity demand for EV charging in the future. Recently, AI techniques, such as neural networks, machine learning, and deep learning, have shown promise in leveraging extensive historical datasets to identify patterns and generate precise predictions. This research paper introduces a novel implementation of an XGBoost regression tree-based algorithm applied to three real-world datasets to estimate the future electric load of EVs. Experimental results demonstrate the superior performance of the proposed algorithm compared to eight state-of-the-art machine learning algorithms, evaluated using the root-mean-squared error and mean absolute error. Furthermore, the proposed algorithm outperforms previous studies in this field, highlighting its effectiveness in forecasting EV charging power requirements.