Data-Driven, Short-Term Prediction of Charging Station Occupation

Abstract

Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events.

Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations

“…In ref. [19], the authors propose a novel model for estimating the duration of charging events in real-time, which enables the prediction of user wait times at fully occupied charging stations. The model is based on a random forest regressor (RF) that is enhanced through automatic data augmentation.…”

“…Improved user decision making: The integration of real-time predictions from Papers [19,20,24] into the user-centric approach can inform users of optimal charging times based on renewable availability, waiting times, and station occupancy.…”

Selected Metal Materials in Automotive Electrical Engineering—A Brief Overview of the State of the Art

“…This has a positive impact on sustainable means of production as it reduces the amount of workspace and lowers energy and manpower consumption. The industry faces significant challenges related to the development of value chains [85], supply chains [86,87], secure data transfer and data management [88], electrification of industry [89], including vehicles and charging stations [90], battery life cycle management [91] and sustainable business practices [92], which are to lead to a reduction in carbon dioxide emissions and a reduction in production space [93] (Figure 2). Cars, not only electric ones, are changing as a result of the expectations of customers, investors, and even employees.…”

Demand Time Series Prediction of Stacked Long Short-Term Memory Electric Vehicle Charging Stations Based on Fused Attention Mechanism

“…Nevertheless, challenges arise concerning capturing long-term dependencies or addressing gradient vanishing issues [48]. Long short-term memory recurrent neural network (LSTM) models [49] have proven to be effective extensions of RNNs for accurate time series prediction tasks. Attention mechanisms are considered crucial components within neural networks to enhance the model's ability to differentiate input data, often utilized in natural language processing (NLP).…”