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

arXiv: https://arxiv.org/abs/2106.04986 · HTML: https://mlsanity.com/paper/2106.04986/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [Prediction of Sea Surface Temperature using Long Short-Term Memory](https://mlsanity.com/paper/1705.06861/) (2017)
- [Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network](https://mlsanity.com/paper/1903.02791/) (2019)
- [Optimal Charging of an Electric Vehicle using a Markov Decision Process](https://mlsanity.com/paper/1310.6926/) (2013)
- [Optimal queueing-based rebalancing for one-way electric carsharing systems with stochastic demand](https://mlsanity.com/paper/2106.02815/) (2021)
- [Optimal fast charging station locations for electric ridesharing service with online vehicle-charging station assignment](https://mlsanity.com/paper/2008.05949/) (2020)
- [Two-stage battery recharge scheduling and vehicle-charger assignment policy for dynamic electric dial-a-ride services](https://mlsanity.com/paper/2010.01541/) (2020)
- [Short-term forecast of EV charging stations occupancy probability using big data streaming analysis](https://mlsanity.com/paper/2104.12503/) (2021)

## Built on by (most cited first, 8 of 8)

- [Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks](https://mlsanity.com/paper/2405.00742/) (2024)
- [Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand](https://mlsanity.com/paper/2301.06418/) (2023)
- [Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge](https://mlsanity.com/paper/2306.06142/) (2023)
- [AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting](https://mlsanity.com/paper/2209.03356/) (2022)
- [Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection](https://mlsanity.com/paper/2204.13702/) (2022)
- [A Data-Driven Framework for Improving Public EV Charging Infrastructure: Modeling and Forecasting](https://mlsanity.com/paper/2312.05333/) (2023)
- [Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences](https://mlsanity.com/paper/2410.18766/) (2024)
- [Electric Vehicle Charging Load Forecasting: An Experimental Comparison of Machine Learning Methods](https://mlsanity.com/paper/2512.17257/) (2025)

## Abstract (arXiv)

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.