Stacking regression technology with event profile for electric vehicle fast charging behavior prediction
Dingsong Cui,
Zhenpo Wang,
Peng Liu,
Shuo Wang,
Yiwen Zhao and
Weipeng Zhan
Applied Energy, 2023, vol. 336, issue C, No S0306261923001629
Abstract:
Large-scale deployment of electric vehicles (EVs) poses a huge challenge to the operation of the distribution network. As a possible mobile energy carrier, the interaction between EVs and distribution networks can provide some opportunities for power operation. Where to charge and how to charge have become an important research topic in EV charging scheduling. Previous studies mainly focused on slow-charging behavior analysis rather than fast-charging behavior. Here, we provide an in-depth understanding of EV user fast-charging behavior in public stations based on more than 220,000 real-world charging records with the Variational-Bayesian Gaussian-mixture model. Characteristics related to charging energy and charging duration are mainly considered in the cluster model, especially dwelling duration after charging is taken into account to better support the decision of charging recommendation strategy and charging power allocation. Inspired by the future application scenario of the charging behavior cluster of previous studies, we propose a charging behavior prediction framework considering behavior catalogues with stacking regression technology. The results show that the proposed framework improves the prediction accuracy of charging behavior and can effectively evaluate the priority of charging behavior.
Keywords: Electric vehicle; Charging behavior clustering; Behavior prediction; Stacking regression model (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261923001629
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:336:y:2023:i:c:s0306261923001629
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2023.120798
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).