Performance analysis of machine learning algorithms for estimation of EV penetration

The escalating threat of global warming poses a formidable challenge to sustainability, necessitating a transformative shift in the transportation sector. A pivotal solution is transitioning from conventional fuel-based vehicles to electric vehicles (EVs) to curtail global warming and unlock significant social and economic benefits. However, this transition could be more straightforward and consists of many challenges, with a major concern being the accurate estimation of the EV population on our roads. Many parameters influence EV adoption, making it crucial to gauge the potential number of EVs on the road. To address this, our study delves into the depths of machine learning, conducting a survey to estimate the EV penetration of the Uttarakhand region in India by employing different ML algorithms, including random forest, support vector machine (SVM), decision trees, artificial neural networks (ANN), and K-nearest neighbor. After estimating EV penetration, an approach to determine the energy and power requirements in the grid infrastructure is shown, considering the domestic EV charging scenario. The study shows that the SVM and ANN algorithms can estimate EV penetration, achieving a higher R-square score of 0.979 and 0.978, respectively, with less root mean square error.