International Journal For Multidisciplinary Research

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Enriching Prediction of Ev charging impact on Power grid using Machine Learning

Author(s) Shubham Gade, Yogesh shivaji kasar, Vinayak Jadhav, Swapnil Raghunath Pokharkar
Country United States
Abstract Lithium mining has been extremely successful, resulting in the production of high-quality batteries across all industries. The major segment of this is electric vehicles; this is again due to fine-tuned innovations in the manufacturing of electric vehicles, which are in all senses more worthwhile than that of fossil fuel vehicles. Electric vehicles outperform fossil fuel cars in terms of mileage, resulting in lower fuel costs for the customer, reduced air and noise pollution, and numerous other advantages over traditional fossil fuel-powered vehicles. However, as we all know, every advantage also carries some disadvantages. Charging these electric vehicles often consumes too much electricity and causes severe grid failures in local and higher hierarchies. Therefore, predicting the impact on the power grid and stabilizing it through the use of smart grid technologies is crucial. This technology plays a crucial role in managing power crises worldwide. Machine learning plays a crucial role in estimating the impact on the power grid, primarily due to the significant electricity usage by EV charging stations. To deploy the model, a dataset of the charging station at the rest area in and around California is collected and weaved with the XGBoost Machine learning model and fuzzy logic concept to predict the impact on the power grid so that they can smartly manage the power crisis.
Keywords Ev Charging impact, Power grid, Shannon Information gain, XF Boost machine learning model, Fuzzy classification
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-25
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.33959
Short DOI https://doi.org/g8w2tx
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