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

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.

Legal actions in Brazilian air transport: A machine learning and multinomial logistic regression analysis

“…These models are widely used in the literature in several domains, such as recommendation systems by Rrmoku et al (2022), speech detection by Alaoui et al (2022), road traffic accidents by Bokaba et al (2022), air pollution prediction by Kumar and Pande (2022), charging demand of electric buses by Deb and Gao (2022), prediction of risk of unmanned aircraft by Truong and Choi (2020), prediction of demand for air taxi aviation by Rajendran et al (2021). The performance results are evaluated based on the confusion matrix, the accuracy, the area under the ROC curve, and the algorithms' processing time.…”