Xai to Support K-Mean Algorithm for Analysis of Real Evs Charging Station Data

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Xai to Support K-Mean Algorithm for Analysis of Real Evs Charging Station Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Xai to Support K-Mean Algorithm for Analysis of Real Evs Charging Station Data

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

4

Strana od-do

1-4

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Chania

Datum konání akce

15. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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