Predicting Charging Demand for Electric Vehicle using Ensemble Learning as Strategy towards Smart Transportation

Abubakar Hassan, Muhammad Aji Wajiro, Yusuf Ayuba, Emmanuel Gbenga Dada

Abstract


Electric vehicles (EVs) are seen as the future of the automobile industry because of their advantage of zero emissions and capability to solve the environmental and public health issues associated with conventional fuel-powered vehicles. The rapid adoption of EVs by many people around the world has led to charging demand prediction for optimal charging infrastructure planning and operation. Predicting charging demand can help enhance operations, mitigate against power outage and grid overloading, and support organisations in computing the amount of charging stations needed to satisfy demand. This paper proposes an ensemble learning model to predict the charging demand of EVs using dataset obtained from Kaggle. Four Machine learning algorithms were employed as base learners namely, Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbour(k-NN) and Gradient Boosting Machine (GBM).  Experimental results showed that SVM achieves excellent performance in comparison to other machine learning algorithms considered in the research with a MASE of 0.706. This shows that SVM is good technique for predicting EV charging demand if adopted.


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References


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