Data-Driven EV Charging Load Forecasting and Smart Charging

Xuewu Dai*, Handong Li, Israel Olanrewaju, Richard Kotter, Ghanim Putrus, Nauman Aslam, Edward Bentley, Yue Wang, Mousa Marzband, Ridoy Das, Jorden Van der Hoogt, Sture Portvik

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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Electrical Vehicles (EVs) have been proposed as a solution for decarbonizing road transport. Smart charging is ebential to coordinate EV energy demand with the requisite peak power supply. The performance of smart charging highly depends on understanding the EVs' charging behaviour (charging patterns and energy demands), and an accurate forecasting of the EV energy demands are ebential for designing a smart charging scheme. This paper presents findings from analysing 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.

Original languageEnglish
Pages (from-to)2832-2839
Number of pages8
JournalTransportation Research Procedia
Early online date13 Dec 2023
Publication statusPublished - 2023
Event2022 Conference Proceedings Transport Research Arena, TRA Lisbon 2022 - Lisboa, Portugal
Duration: 14 Nov 202217 Nov 2022

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