Data-Driven EV Charging Load Forecasting and Smart Charging

Electrical Vehicles (EVs) have been proposed as a solution for decarbonizing road transport. Smart charging is essential to coordinate EV energy demand with the requisite peak power supply. The performance of smart charging highly depends on understanding the EVs’ charging behavior (charging patterns and energy demands), and an accurate forecasting of the EV energy demands are essential for designing a smart charging scheme. This paper presents findings from analyzing 3 years’ data of an Oslo Vulkan parking garage pilot, one of the largest hybrid public/commercial/residential parking garages for EV charging in Norway/Europe. A long-short-term-memory (LSTM) regression network is developed to predict hourly EV charging demand with a Weighted Absolute Percentage Error of 30.5%. The analysis suggests that a smart charging strategy is needed to shave the peak demand during 19:00-21:00.

• Record URL:
  • https://doi.org/10.1016/j.trpro.2023.11.827
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2352146523011250
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2023 The Authors. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Dai, Xuewu
  • Li, Handong
  • Olanrewaju, Israel
  • Kotter, Richard
  • Putrus, Ghanim
  • Aslam, Nauman
  • Bentley, Edward
  • Wang, Yue
  • Marzband, Mousa
  • Das, Ridoy
  • van der Hoogt, Jorden
  • Portvik, Sture
• Conference:
  • Transport Research Arena Conference (TRA Lisbon 2022)
  • Location: Lisbon , Portugal
  • Date: 2022-11-14 to 2022-11-17
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2832-2839
• Serial:
  • Transportation Research Procedia
  • Volume: 72
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Demand; Electric vehicle charging; Parking garages; Predictive models; Vehicle design
• Geographic Terms: Norway
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01909760
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 26 2024 8:51AM