Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00379262" target="_blank" >RIV/68407700:21230/24:00379262 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/24:00379262

Result on the web

<a href="https://www.climatechange.ai/papers/neurips2024/22" target="_blank" >https://www.climatechange.ai/papers/neurips2024/22</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas

Original language description

This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to uncover latent charging profiles influenced by spatio-temporal factors. Our model focuses on peak power demand and daily load shapes, providing insights into charging behavior. Our results indicate significant impacts from the type of Basic Administrative Units on predicted load curves, which contributes to the understanding and optimization of EV charging infrastructure in urban settings and allows Distribution System Operators (DSO) to more efficiently plan EV charging infrastructure expansion.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/TS01020030" target="_blank" >TS01020030: The use of Vehicle-to-Grid technology to provide energy flexibility</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

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