Forecasting Charging Demand of Electric Vehicles Using Time-Series Models

This study compared the methods used to forecast increases in power consumption caused by 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 various factors, including condition a vehicle, battery’s state-of-charge (SOC), and distance destination. However, suppliers cannot easily access this information due privacy issues. Despite lack individual information, modeling techniques, trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), long short-term memory (LSTM) modeling, based on past values exogenous variables. The effect variables evaluated macro- micro-scale areas, importance historic data verified. basic statistics regarding number stations volume expected aid formulation method that can be suppliers.