Estimating increase of electric energy according to penetration of electric vehicles at the Jeju Island in Korea

Kim, Su-Wan; Lee, Gae-Myoung

doi:10.1109/itec-ap.2016.7513086

Cited by 1 publication

(1 citation statement)

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“…However, the model's limitation is its scalability for larger and complex power systems. In [5] prediction of increase in energy consumption due to EV penetration is conducted on Jeju Island, Korea. The prediction is performed by employing statistical analysis and fuel economy data.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Machine Learning Algorithms for Estimation of EV Penetration

Chhetri,

Saini,

Yadav

et al. 2024

Preprint

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The escalating threat of global warming poses a formidable challenge to sustainability, necessitating a transformative shift in the transportation sector. A pivotal solution lies in transitioning from conventional fuel-based vehicles to electric vehicles (EVs) to curtail global warming and unlock significant social and economic benefits. However, this transition is far from 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 (ML), conducting a study to estimate the EV penetration of the Uttarakhand region in India by employing different ML algorithms, including random forest (RF), support vector machine (SVM), decision trees, artificial neural networks (ANN), and K-nearest neighbor (KNN). After the estimation of 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 be used for the estimation of EV penetration, achieving a higher R-square score of 0.979 and 0.978 respectively, with less root mean square error (RMSE).

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Section: Literature Reviewmentioning

confidence: 99%