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

TL;DR

This paper introduces a hybrid LSTM neural network for multistep EV charging station occupancy prediction, significantly improving accuracy over existing methods by incorporating feature separation and time-related data.

Contribution

The study presents a novel mixed LSTM model that separates feature types and handles them differently, enhancing multistep occupancy prediction accuracy.

Findings

Achieved 99.99% accuracy for 10-minute ahead prediction.

Outperformed benchmark models by 22.4% for 1-step prediction.

Demonstrated robustness through sensitivity analysis.

Abstract

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.