Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks
This work addresses the need for accurate occupancy predictions to reduce inconvenience for EV operators and users, representing an incremental improvement over existing methods.
The paper tackled the problem of predicting electric vehicle charging station occupancy by proposing a hybrid LSTM neural network that incorporates historical charging sequences and time-related features, achieving high accuracy with 99.99% for 10-minute predictions and 81.87% for 1-hour predictions, outperforming benchmarks by up to 22.4%.
Public charging station occupancy prediction plays key importance in developing a smart charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However, existing studies are mainly based on conventional econometric or time series methodologies with limited accuracy. We propose a new mixed long short-term memory neural network incorporating both historical charging state sequences and time-related features for multistep discrete charging occupancy state prediction. Unlike the existing LSTM networks, the proposed model separates different types of features and handles them differently with mixed neural network architecture. The model is compared to a number of state-of-the-art machine learning and deep learning approaches based on the EV charging data obtained from the open data portal of the city of Dundee, UK. The results show that the proposed method produces very accurate predictions (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). A sensitivity analysis is conducted to evaluate the impact of the model parameters on prediction accuracy.