Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand

Sahar Koohfar, Wubeshet Woldemariam () and Amit Kumar ()
Additional contact information
Sahar Koohfar: School of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Wubeshet Woldemariam: Mechanical and Civil Engineering Department, Purdue University Northwest, Hammond, IN 46323, USA
Amit Kumar: School of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA

Sustainability, 2023, vol. 15, issue 5, 1-20

Abstract: Electric 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.

Keywords: electric vehicle (EV); RNN; LSTM; Bi-LSTM; GRU; CNN; transformers; machine learning; time series (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/5/4258/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/5/4258/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:5:p:4258-:d:1082238

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().