Charging Data Analysis and Classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256366" target="_blank" >RIV/61989100:27240/24:10256366 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27730/24:10256366
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772676&utm_source=scopus&getft_integrator=scopus" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772676&utm_source=scopus&getft_integrator=scopus</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/INDEL62640.2024.10772676" target="_blank" >10.1109/INDEL62640.2024.10772676</a>

Result language
angličtina
Original language name
Charging Data Analysis and Classification
Original language description
This study develops a classification model for Electric Vehicles (EVs) using data from EV charging stations. Utilizing a dataset of six charging sessions, a deep learning algorithms, Auto-Encoders, was implemented to classify EVs based on charging time, energy consumption, and charging patterns. The model's effectiveness is assessed through accuracy and precision, offering insights into optimizing EV charging infrastructure and services.
Czech name
—
Czech description
—

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2024 15th International Symposium on Industrial Electronics and Applications, INDEL 2024 : Proceedings
ISBN
979-8-3503-5233-7
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
1-6
Publisher name
IEEE
Place of publication
Piscataway
Event location
Banja Luka
Event date
Nov 6, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)