K-Means Clustering for Identification of Household Energy Utilization and Production

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260638" target="_blank" >RIV/61989100:27240/25:10260638 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11169119" target="_blank" >https://ieeexplore.ieee.org/document/11169119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EEEIC/ICPSEurope64998.2025.11169119" target="_blank" >10.1109/EEEIC/ICPSEurope64998.2025.11169119</a>

Jazyk výsledku

angličtina

Název v původním jazyce

K-Means Clustering for Identification of Household Energy Utilization and Production

Popis výsledku v původním jazyce

This paper presents an analysis of household different energy situations using the k-means clustering algorithm, based on real-world data collected in Dingle, Ireland. The study begins with an overview of the growing importance of photovoltaic (PV) installations in the residential energy balance. The core of the work focuses on a single household equipped with a PV system and an energy storage unit, monitored throughout the entire year of 2020. The collected time-series data undergo preprocessing to ensure quality and consistency. Subsequently, the k-means clustering method is applied to identify typical patterns and distinct situations of household energy usage. The resulting clusters are visualized and interpreted to gain insights into the household&apos;s operational behaviour under different energy scenarios. This type of research is crucial to support the integration of single households into efficient energy communities. .

Název v anglickém jazyce

K-Means Clustering for Identification of Household Energy Utilization and Production

Popis výsledku anglicky

This paper presents an analysis of household different energy situations using the k-means clustering algorithm, based on real-world data collected in Dingle, Ireland. The study begins with an overview of the growing importance of photovoltaic (PV) installations in the residential energy balance. The core of the work focuses on a single household equipped with a PV system and an energy storage unit, monitored throughout the entire year of 2020. The collected time-series data undergo preprocessing to ensure quality and consistency. Subsequently, the k-means clustering method is applied to identify typical patterns and distinct situations of household energy usage. The resulting clusters are visualized and interpreted to gain insights into the household&apos;s operational behaviour under different energy scenarios. This type of research is crucial to support the integration of single households into efficient energy communities. .

Druh

D - Stať ve sborníku

CEP obor

—

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

6

Strana od-do

1-6

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

—