Abstract
As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, is the strain on power grid infrastructure that comes with large-scale EV deployment. The solution to this lies in utilization of smart scheduling algorithms to manage the growing public charging demand. Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. Researchers have focused on using historical charging data for predictions of behavior such as departure time and energy needs. However, variables such as weather, traffic, and nearby events, which have been neglected to a large extent, can perhaps add meaningful representations, and provide better predictions. Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. The best predictive performance is achieved by an ensemble learning model, with SMAPE scores of 9.9% and 11.6% for session duration and energy consumptions, respectively, which improves upon the existing works in the literature. In both predictions, we demonstrate a significant improvement compared to previous work on the same dataset and we highlight the importance of traffic and weather information for charging behavior predictions.
Highlights
Climate change has become a growing concern in recent years with thirty-three countries jointly declaring a climate emergency as of January 2021 [1]
Studies have shown that electric vehicles (EVs) have the potential to reduce carbon emissions by 45% compared to conventional internal combustion engine (ICE) vehicles [4]
They experimented with pattern sequence-based forecasting (PSF) [29], where clustering is first applied to classify the days and predictions are made for that day
Summary
Climate change has become a growing concern in recent years with thirty-three countries jointly declaring a climate emergency as of January 2021 [1]. Examples of other charging behavior include the prediction of whether the EVs will be charged the day [15], identification of the use of fast charging [16], prediction of the time to plug [17], charge profile prediction [18], charging speed prediction [19] and prediction of charging capacity and the daily charging times [20] These behaviors provide valuable insights, but the prediction of session duration and energy time is more valuable for scheduling purposes. Lee et al [21] introduced a novel dataset for non-residential EV charging consisting of over 30000 charging sessions They used gaussian mixture models (GMM) to predict session duration and energy needs by considering the distribution of the known arrival times. The ensemble model performed better than the individual models in both predictions and the reported SMAPEs were 10.4% for duration and 7.5% for the consumption
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