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

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

doi:10.1016/j.procs.2018.04.080

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

Guus Berkelmans, Wouter Berkelmans, Nanda Piersma, Rob van der Mei, Elenna Dugundji

Research output: Contribution to journal › Article › Academic › peer-review

2 Citations (Scopus)

113 Downloads (Pure)

Abstract

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

Original language	English
Pages (from-to)	549–556
Number of pages	8
Journal	Procedia Computer Science
Volume	130
DOIs	https://doi.org/10.1016/j.procs.2018.04.080
Publication status	Published - 2018
Event	9th International Conference on Ambient Systems, Networks and Technologies - Porto, Portugal Duration: 8 May 2018 → 11 May 2018

Access to Document

10.1016/j.procs.2018.04.080Licence: CC BY-NC-ND

1-s2.0-S1877050918304423-mainFinal published version, 272 KBLicence: CC BY-NC-ND

Handle.net

Persistent link

Cite this

@article{3a39e436ab314287a2bddf0dad9394d1,

title = "Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects",

abstract = "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",

keywords = "Urban Analytics",

author = "Guus Berkelmans and Wouter Berkelmans and Nanda Piersma and Mei, {Rob van der} and Elenna Dugundji",

note = "Part of special issue: The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) In conjunction with The 8th International Conference on Sustainable Energy Information Technology (SEIT-2018) / Affiliated Workshops Edited by Elhadi Shakshuki, Ansar Yasar; 9th International Conference on Ambient Systems, Networks and Technologies, ANT 2018 ; Conference date: 08-05-2018 Through 11-05-2018",

year = "2018",

doi = "10.1016/j.procs.2018.04.080",

language = "English",

volume = "130",

pages = "549–556",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier",

}

TY - JOUR

T1 - Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects

AU - Berkelmans, Guus

AU - Berkelmans, Wouter

AU - Piersma, Nanda

AU - Mei, Rob van der

AU - Dugundji, Elenna

N1 - Part of special issue: The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) In conjunction with The 8th International Conference on Sustainable Energy Information Technology (SEIT-2018) / Affiliated Workshops Edited by Elhadi Shakshuki, Ansar Yasar

PY - 2018

Y1 - 2018

N2 - 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

AB - 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

KW - Urban Analytics

U2 - 10.1016/j.procs.2018.04.080

DO - 10.1016/j.procs.2018.04.080

M3 - Article

SN - 1877-0509

VL - 130

SP - 549

EP - 556

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 9th International Conference on Ambient Systems, Networks and Technologies

Y2 - 8 May 2018 through 11 May 2018

ER -

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

Abstract

Access to Document

Handle.net

Fingerprint

Cite this