Abstract
This study compared the methods used to forecast increases in power consumption caused by the rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced by various factors, including the condition of a vehicle, the battery’s state-of-charge (SOC), and the distance to the destination. However, power suppliers cannot easily access this information due to privacy issues. Despite a lack of individual information, this study compared various modeling techniques, including trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, and Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and long short-term memory (LSTM) modeling, based on past values and exogenous variables. The effect of exogenous variables was evaluated in macro- and micro-scale geographical areas, and the importance of historic data was verified. The basic statistics regarding the number of charging stations and the volume of charging in each region are expected to aid the formulation of a method that can be used by power suppliers.
Highlights
This study proposes an optimal forecasting model for electric vehicle power suppliers regarding each electric vehicles (EVs) charging unit in a country, city, or single charging station based on realworld data
We find that the artificial neural networks (ANN) model gave the lowest error and showed robustness compared to the statistical time series models
There was no significant difference between the short-term predictions using TBATS and autoregressive integrated moving average (ARIMA); the shorter history provided sufficient information
Summary
The advent of electric vehicles (EVs) in the 19th century [1,2] has since posed a growing challenge to the current automobile industry. Considerable research and development have facilitated significant progress, thereby overcoming several issues associated with EV batteries These advances have allowed the EV market to compete with, and in some cases overtake, the combustion-engine automotive industry [3]. Several governments have implemented regulations, incentives, and industry promotions [4] to encourage the effective use of EVs. In addition to economic policies regarding electric vehicles, the supporting infrastructure, including sufficient charging stations and stable power supply in buildings and roads, should be provided
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