Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand

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dc.contributor.authorKoohfar, Sahar
dc.contributor.authorWoldemariam, Wubeshet
dc.contributor.authorKumar, Amit
dc.date.accessioned2023-03-10T14:02:24Z
dc.date.available2023-03-10T14:02:24Z
dc.date.issued2023-02-27
dc.date.updated2023-03-10T14:02:26Z
dc.description.abstractElectric vehicles (EVs) contribute to reducing fossil fuel dependence and environmental pollution problems. However, due to complex charging behaviors and the high demand for charging, EVs have imposed significant burdens on power systems. By providing reliable forecasts of electric vehicle charging loads to power systems, these issues can be addressed efficiently to dispatch energy. Machine learning techniques have been demonstrated to be effective in forecasting loads. This research applies six machine learning methods to predict the charging demand for EVs: RNN, LSTM, Bi-LSTM, GRU, CNN, and transformers. A dataset containing five years of charging events collected from 25 public charging stations in Boulder, Colorado, USA, is used to validate this approach. Compared to other highly applied machine learning models, the transformer method outperforms others in predicting charging demand, demonstrating its ability for time series forecasting problems.
dc.description.departmentCivil and Environmental Engineering, and Construction Management
dc.identifierdoi: 10.3390/su15054258
dc.identifier.citationSustainability 15 (5): 4258 (2023)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1795
dc.rightsAttribution 4.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectelectric vehicle (EV)
dc.subjectRNN
dc.subjectLSTM
dc.subjectBi-LSTM
dc.subjectGRU
dc.subjectCNN
dc.subjecttransformers
dc.subjectmachine learning
dc.subjecttime series
dc.titlePerformance Comparison of Deep Learning Approaches in Predicting EV Charging Demand
dc.typeArticle

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