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Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects

Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects

Samenvatting

In the past 5 years Electric Car use has grown rapidly, almost doubling each year. To provide adequate charging infrastructure it is necessary to model the demand. In this paper we model the distribution of charging demand in the city of Amsterdam using a Cross-Nested Logit Model with socio-demographic statistics of neighborhoods and charging history of vehicles. Models are obtained for three user-types: regular users, electric car-share participants and taxis. Regular users are later split into three subgroups based on their charging behaviour throughout the day: Visitors, Commuters and Residents

Trefwoorden
OrganisatieHogeschool van Amsterdam
AfdelingKenniscentrum Techniek
LectoraatLectoraat Urban Analytics
Gepubliceerd inProcedia Computer Science Elsevier, Vol. 130
Jaar2018
TypeArtikel
ISSN1877-0509
DOI10.1016/j.procs.2018.04.080
TaalEngels

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