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

Guus Berkelmans | Zendy; Wouter Berkelmans | Zendy; Nanda Piersma | Zendy; Rob van der Mei | Zendy; Elenna Dugundji | Zendy

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

Author(s) -

Guus Berkelmans,

Wouter Berkelmans,

Nanda Piersma,

Rob van der Mei,

Elenna Dugundji

Publication year - 2018

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2018.04.080

Subject(s) - taxis , computer science , nested logit , electric vehicle , logistic regression , generalized extreme value distribution , econometrics , value (mathematics) , statistics , extreme value theory , transport engineering , power (physics) , mathematics , machine learning , physics , quantum mechanics , engineering

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.

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