Electric vehicle owners face the problem of having limited charging station options. Individual charging stations near households can act as a viable solution to solve this problem. A forecasting model which can effectively predict the power consumption of a charging station will help charging station owners get a clear view of how much energy to produce.With this intent, this paper proposes an Internet of Things (IoT) based charging station network that acts as a platform to provide charging to electric vehicles and a model based on ARIMA whose learners are fitted to the charging station subsets with optimum parameters to increase the overall performance of sales prediction. The proposed model predicted power consumption for 7 charging stations, with average MAPE, RMSE and R2 values of 12.88%, 5.67, and 0.79 respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.