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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
K-Mean Algorithm to Support Energy Future Decision for Household with PV and EV
Original language description
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.
Czech name
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Czech description
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Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
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
Number of pages
4
Pages from-to
1-4
Publisher name
IEEE
Place of publication
Piscataway
Event location
Řím
Event date
Jun 17, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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