Xai to Support K-Mean Algorithm for Analysis of Real Evs Charging Station Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258749" target="_blank" >RIV/61989100:27240/25:10258749 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11169023" target="_blank" >https://ieeexplore.ieee.org/document/11169023</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EEEIC/ICPSEurope64998.2025.11169023" target="_blank" >10.1109/EEEIC/ICPSEurope64998.2025.11169023</a>
Alternative languages
Result language
angličtina
Original language name
Xai to Support K-Mean Algorithm for Analysis of Real Evs Charging Station Data
Original language description
This paper explores the application of explainable artificial intelligence (XAI) as a supportive tool for the K-means clustering algorithm which analyze electric vehicles (EVs) chagrining station energy data. Under the investigation the Decision Tree were used to explain the process of clustering assignment. Under the case study investigation six real EVs charging station data were used in area-related approach. The results indicated that proposed solution for arearelated approach can be implemented for real case objects and using XAI. Also the results of clustering indicated the real working condition of ECs charging station that are supportive in decision making.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE International Conference on Environment and Electrical Engineering and 2025 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2025 : conference proceedings : 15-18 July 2025, Chania, Crete-Greece
ISBN
979-8-3315-9516-6
ISSN
2994-9440
e-ISSN
2994-9467
Number of pages
4
Pages from-to
1-4
Publisher name
IEEE
Place of publication
Piscataway
Event location
Chania
Event date
Jul 15, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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