Electric vehicle charging demand forecasting using deep learning model

Abstract

Greenhouse gas (GHG) emission and excessive fuel consumption have become a pressing issue nowadays. Particularly, CO₂ emissions from transportation account for approximately one-quarter of global emissions since 2016. Electric vehicle (EV) is considered an appealing option to address the aforementioned concerns. However, with the growing EV market, issues such as insufficient charging infrastructure to support such ever-increasing demand emerge as well. Effectively forecasting the commercial EV charging demand ensures the reliability and robustness of grid utility in the short term and helps with investment planning and resource allocation for charging infrastructures in the long run. To this end, this article presents a time-series forecasting of the monthly commercial EV charging demand using a deep learning approach-Sequence to Sequence (Seq2Seq). The proposed model is validated by real-world datasets from the State of Utah and the City of Los Angeles. Two prediction targets, namely one-step ahead prediction and multi-step ahead prediction, are tested. Further, the model is benchmarked and compared against other time series and machine learning models. Experiments show that both Seq2seq and long short-term memory (LSTM) generate satisfactory prediction performance for one-step prediction. However, when performing the multi-step prediction, Seq2Seq significantly outperforms other models in terms of various performance metrics, indicating the model’s strong capability for sequential data predictions.

Keywords:

• charging demand forecast
• deep learning
• electric vehicle (EV)
• sequence to sequence (Seq2Seq) model

Disclosure statement

No potential conflict of interest was reported by the author(s).