# Short-term forecast of EV charging stations occupancy probability using big data streaming analysis

arXiv: https://arxiv.org/abs/2104.12503 · HTML: https://mlsanity.com/paper/2104.12503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 12 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks](https://mlsanity.com/paper/2106.04986/) (2021)

## Abstract (arXiv)

The widespread diffusion of electric mobility requires a contextual expansion of the charging infrastructure. An extended collection and processing of information regarding charging of electric vehicles may turn each electric vehicle charging station into a valuable source of streaming data. Charging point operators may profit from all these data for optimizing their operation and planning activities. In such a scenario, big data and machine learning techniques would allow valorizing real-time data coming from electric vehicle charging stations. This paper presents an architecture able to deal with data streams from a charging infrastructure, with the final aim to forecast electric charging station availability after a set amount of minutes from present time. Both batch data regarding past charges and real-time data streams are used to train a streaming logistic regression model, to take into account recurrent past situations and unexpected actual events. The streaming model performs better than a model trained only using historical data. The results highlight the importance of constantly updating the predictive model parameters in order to adapt to changing conditions and always provide accurate forecasts.