Prediction of EV Charging Behavior Using Machine Learning Article Swipe
Sakib Shahriar
,
A. R. Al-Ali
,
Ahmed Osman
,
Salam Dhou
,
Mais Nijim
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3103119
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3103119
As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, is the strain on power grid infrastructure that comes with large-scale EV deployment. The solution to this lies in utilization of smart scheduling algorithms to manage the growing public charging demand. Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. Researchers have focused on using historical charging data for predictions of behavior such as departure time and energy needs. However, variables such as weather, traffic, and nearby events, which have been neglected to a large extent, can perhaps add meaningful representations, and provide better predictions. Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. The best predictive performance is achieved by an ensemble learning model, with SMAPE scores of 9.9% and 11.6% for session duration and energy consumptions, respectively, which improves upon the existing works in the literature. In both predictions, we demonstrate a significant improvement compared to previous work on the same dataset and we highlight the importance of traffic and weather information for charging behavior predictions.
Related Topics
Concepts
Computer science
Software deployment
Smart grid
Scheduling (production processes)
Machine learning
Random forest
Support vector machine
Artificial intelligence
Key (lock)
Energy consumption
Deep learning
Session (web analytics)
Computer security
Engineering
Operations management
World Wide Web
Operating system
Electrical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3103119
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09508419.pdf
- OA Status
- gold
- Cited By
- 180
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3189940786
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3189940786Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3103119Digital Object Identifier
- Title
-
Prediction of EV Charging Behavior Using Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Sakib Shahriar, A. R. Al-Ali, Ahmed Osman, Salam Dhou, Mais NijimList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3103119Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09508419.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09508419.pdfDirect OA link when available
- Concepts
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Computer science, Software deployment, Smart grid, Scheduling (production processes), Machine learning, Random forest, Support vector machine, Artificial intelligence, Key (lock), Energy consumption, Deep learning, Session (web analytics), Computer security, Engineering, Operations management, World Wide Web, Operating system, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
180Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 68, 2024: 64, 2023: 31, 2022: 15, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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