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Title	Machine Learning-Based Decision Making For Charging/Discharging Cost Optimization Of Prev In Smart City
ID_Doc	36044
Authors	Neelakantam G.
Year	2024
Published	Proceedings of the 1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024
DOI	http://dx.doi.org/10.1109/PCDS61776.2024.10743752
Abstract	Smart cities, equipped with smart grid infrastructure, establish advanced communication networks that facilitate interactions among multiple entities. It is well-known that smart grid technology ensures the reliable transmission of electricity. Additionally, Plug-in Hybrid Electric Vehicles (PHEVs) significantly contribute to the efficient utilization of energy in mobility-aware environments. The Intelligent Transportation System (ITS) enables Vehicle-to-Infrastructure (V2I) communication, which is crucial for providing transportation services and managing PHEV recharging at user-preferred locations. This capability is a key element of smart city infrastructure. To optimize the use of PHEV services in a smart city, addressing the cost minimization of charging and discharging is essential. Therefore, this paper proposes a decision tree machine learning-based algorithm within a fog computing platform aimed at minimizing charging and discharging costs. Performance evaluation demonstrates that the model outperforms existing algorithms in terms of accuracy. These results indicate that our model can accurately predict the costs associated with charging and discharging PHEVs in smart city environments. © 2024 IEEE.
Author Keywords	Charging; Discharging; Machine Learning; PHEV; Pricing Model; Smart City

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