K-Mean Algorithm to Support Energy Future Decision for Household with PV and EV

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256465" target="_blank" >RIV/61989100:27240/24:10256465 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10751226" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10751226</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EEEIC/ICPSEurope61470.2024.10751226" target="_blank" >10.1109/EEEIC/ICPSEurope61470.2024.10751226</a>

Jazyk výsledku

angličtina

Název v původním jazyce

K-Mean Algorithm to Support Energy Future Decision for Household with PV and EV

Popis výsledku v původním jazyce

This paper explores the application of the K-means clustering algorithm to analyze household energy data, focusing on electricity demand, photovoltaic (PV) generation, and electric vehicle (EV) charging. The objective is to identify distinct patterns of energy usage and generation, which can inform better energy management and decision-making strategies. Using data from a London household, we apply K-means clustering to segment the energy usage into meaningful clusters. The analysis reveals distinct profiles corresponding to different times of day and types of energy consumption and generation. Key findings suggest that clustering can effectively differentiate between high and low usage periods, the impact of PV generation on household energy dynamics, and the charging patterns of EVs. The results of this study provide valuable insights into how households can optimize energy consumption and leverage their PV and EV systems more effectively. Additionally, the paper discusses the implications of these findings for future energy policy and smart grid development. Recommendations are offered for integrating advanced data analytics into residential energy management systems to enhance sustainability and efficiency.

Název v anglickém jazyce

K-Mean Algorithm to Support Energy Future Decision for Household with PV and EV

Popis výsledku anglicky

This paper explores the application of the K-means clustering algorithm to analyze household energy data, focusing on electricity demand, photovoltaic (PV) generation, and electric vehicle (EV) charging. The objective is to identify distinct patterns of energy usage and generation, which can inform better energy management and decision-making strategies. Using data from a London household, we apply K-means clustering to segment the energy usage into meaningful clusters. The analysis reveals distinct profiles corresponding to different times of day and types of energy consumption and generation. Key findings suggest that clustering can effectively differentiate between high and low usage periods, the impact of PV generation on household energy dynamics, and the charging patterns of EVs. The results of this study provide valuable insights into how households can optimize energy consumption and leverage their PV and EV systems more effectively. Additionally, the paper discusses the implications of these findings for future energy policy and smart grid development. Recommendations are offered for integrating advanced data analytics into residential energy management systems to enhance sustainability and efficiency.

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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024 : conference proceedings

ISBN

979-8-3503-5519-2

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

Řím

Datum konání akce

17. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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