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Public charging station occupancy prediction plays key importance indeveloping a smart charging strategy to reduce electric vehicle (EV) operatorand user inconvenience. However, existing studies are mainly based onconventional econometric or time series methodologies with limited accuracy. Wepropose a new mixed long short-term memory neural network incorporating bothhistorical charging state sequences and time-related features for multistepdiscrete charging occupancy state prediction. Unlike the existing LSTMnetworks, the proposed model separates different types of features and handlesthem differently with mixed neural network architecture. The model is comparedto a number of state-of-the-art machine learning and deep learning approachesbased on the EV charging data obtained from the open data portal of the city ofDundee, UK. The results show that the proposed method produces very accuratepredictions (99.99% and 81.87% for 1 step (10 minutes) and 6 steps (1 hour)ahead, respectively, and outperforms the benchmark approaches significantly(+22.4% for one-step-ahead prediction and +6.2% for 6 steps ahead). Asensitivity analysis is conducted to evaluate the impact of the modelparameters on prediction accuracy.

Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks