Smart Grid Seminar: Public EV Charging in Europe
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00377730" target="_blank" >RIV/68407700:21230/24:00377730 - isvavai.cz</a>
Výsledek na webu
<a href="https://events.stanford.edu/event/smart-grid-seminar-ev-charging-europe" target="_blank" >https://events.stanford.edu/event/smart-grid-seminar-ev-charging-europe</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Smart Grid Seminar: Public EV Charging in Europe
Popis výsledku v původním jazyce
We will explore a study conducted by researchers from CTU Prague and Stanford University, addressing EV charging insights from true datasets from Europe. Based on the insights, we introduce an innovative approach of using machine learning to simulate electric vehicle (EV) charging profiles in urban areas with limited data, a challenge of predicting EV charging behavior influenced by spatio-temporal factors. We will delve into the neural network architecture used to uncover latent charging profiles, focusing on peak power demand and daily load shapes. We highlight the significant impacts of Basic Administrative Units on predicted load curves, providing insights into optimizing EV charging infrastructure. We discuss how this model can help Distribution System Operators (DSOs) efficiently plan EV charging infrastructure expansion in urban settings, as well as balancing opportunities for the grid.
Název v anglickém jazyce
Smart Grid Seminar: Public EV Charging in Europe
Popis výsledku anglicky
We will explore a study conducted by researchers from CTU Prague and Stanford University, addressing EV charging insights from true datasets from Europe. Based on the insights, we introduce an innovative approach of using machine learning to simulate electric vehicle (EV) charging profiles in urban areas with limited data, a challenge of predicting EV charging behavior influenced by spatio-temporal factors. We will delve into the neural network architecture used to uncover latent charging profiles, focusing on peak power demand and daily load shapes. We highlight the significant impacts of Basic Administrative Units on predicted load curves, providing insights into optimizing EV charging infrastructure. We discuss how this model can help Distribution System Operators (DSOs) efficiently plan EV charging infrastructure expansion in urban settings, as well as balancing opportunities for the grid.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů