Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest

Sanchari Deb and Xiao-Zhi Gao
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Sanchari Deb: School of Engineering, University of Warwick, Coventry CV4 7AL, UK
Xiao-Zhi Gao: School of Computing, University of Eastern Finland, 70211 Kuopio, Finland

Energies, 2022, vol. 15, issue 10, 1-18

Abstract: Climate change, global warming, pollution, and energy crisis are the major growing concerns of this era, which have initiated the electrification of transport. The electrification of roadway transport has the potential to drastically reduce pollution and the growing demand for energy and to increase the load demand of the power grid, thereby giving a rise to technological and commercial challenges. Thus, charging load prediction is a crucial and demanding issue for maintaining the security and stability of power systems. During recent years, random forest has gained a lot of popularity as a powerful machine learning technique for classification as well as regression analysis. This work develops a random forest (RF)-based approach for predicting charging demand. The proposed method is validated for the prediction of public e-bus charging demand in the city of Helsinki, Finland. The simulation results demonstrate the effectiveness of our scheme.

Keywords: charger; demand; E bus; random forest; electric vehicle; charging; demand; methodology (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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