Deep Learning and Machine Learning Approaches for Forecasting Electric Vehicle Charging Behaviour

ABSTRACT

The growing shift toward electrifying the transportation sector is driving a significant increase in electricity demand, with large‐scale electric vehicle (EV) charging emerging as a major contributor. This surge is creating new challenges for power distribution systems. One of the key problems is the limited understanding of how EVs impact grid energy consumption, making it difficult to manage and plan for large‐scale charging needs effectively. Traditional methods for electric vehicle charging prediction are difficult to adapt to the dynamic charging behaviours, high volatility of data, and high prediction accuracy requirements, and therefore, more intelligent prediction models based on machine learning and deep learning are needed. It suggests that to meet the projected long‐term energy requirements for EV charging, the future grid must be adequately equipped to handle such a large load.

In this research, we solve this difficulty by constructing analytics‐driven models generated from EV charging trends by applying deep learning (DL) as effectively as the machine learning (ML) techniques. Our objective is to create information that can assist in estimating energy utilisation trends and lessen the possible stress on the grid. First, we examined how extensive EV charging might affect the grid system. Following this research, we proposed an optimisation target that maximises the entire value of EVs charging on the electrical grid while obeying crucial restrictions like voltage reliability and power boundaries. Next, we employed a graphical illustrating approach to evaluate how EV energy utilisation is spread over duration, utilising real‐world information from real electric charging stations. Ultimately, we created forecasting models utilising four distinct deep learning as well as machine learning regression methods: fine decision trees, support vector machine (SVM), linear regression, as well as neural networks. In order to analyse the predictive model, we utilised a 5‐fold cross‐validating approach during training to verify the algorithms could be extended effectively and prevent overfitting. We next examined model efficacy using typical regression measurement techniques. The suggested ML/DL models demonstrated good predictive power with nearly identical R ² values and negligible prediction error rates, proving their effectiveness for dependable EV charging demand forecasting strategies. The findings of our research demonstrated that the algorithms we developed achieved more accuracy in forecasting in comparison with similar studies in the field, suggesting the effectiveness of our technique. The forecasting results are utilised more to increase optimisation through supporting forecasting decision‐making, the best possible utilisation of assets, and improved system performance under diverse operational conditions.