Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93379
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical Engineering | en_US |
dc.creator | Zhang, X | en_US |
dc.creator | Chan, KW | en_US |
dc.creator | Li, H | en_US |
dc.creator | Wang, H | en_US |
dc.creator | Qiu, J | en_US |
dc.creator | Wang, G | en_US |
dc.date.accessioned | 2022-06-21T08:23:19Z | - |
dc.date.available | 2022-06-21T08:23:19Z | - |
dc.identifier.issn | 2168-2267 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93379 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication X. Zhang, K. W. Chan, H. Li, H. Wang, J. Qiu and G. Wang, "Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model," in IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3157-3170, June 2021 is available at https://doi.org/10.1109/TCYB.2020.2975134 | en_US |
dc.subject | Convolutional neural network (CNN) | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Driver behavior | en_US |
dc.subject | Electric vehicle (EV) | en_US |
dc.subject | Load forecast | en_US |
dc.subject | Queuing model | en_US |
dc.title | Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 3157 | en_US |
dc.identifier.epage | 3170 | en_US |
dc.identifier.volume | 51 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.doi | 10.1109/TCYB.2020.2975134 | en_US |
dcterms.abstract | With the emerging electric vehicle (EV) and fast charging technologies, EV load forecasting has become a concern for planners and operators of EV charging stations (CSs). Due to the nonstationary feature of the traffic flow (TF) and the erratic nature of the charging procedures, EV charging load is difficult to accurately forecast. In this article, TF is first predicted using a deep-learning-based convolutional neural network (CNN), and different forecast uncertainties are evaluated to formulate the TF prediction intervals (PIs). Then, the EV arrival rates are calculated according to the historical data and the proposed mixture model. Based on TF forecasting and arrival rate results, the EV charging process is studied to convert the TF to the charging load using a novel probabilistic queuing model that takes into consideration charging service limitations and driver behaviors. The proposed models are assessed using the actual TF data, and the results show that the uncertainties of the EV charging load can be learned comprehensively, indicating significant potential for practical applications. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on cybernetics, June 2021, v. 51, no. 6, 9055130, p. 3157-3170 | en_US |
dcterms.isPartOf | IEEE transactions on cybernetics | en_US |
dcterms.issued | 2021-06 | - |
dc.identifier.scopus | 2-s2.0-85106497867 | - |
dc.identifier.pmid | 32248136 | - |
dc.identifier.eissn | 2168-2275 | en_US |
dc.identifier.artn | 9055130 | en_US |
dc.description.validate | 202206 bchy | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EE-0023 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Foundations of Shenzhen Science and Technology Committee | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 54440561 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Zhang_Deep-Learning-Based_Probabilistic_Forecasting.pdf | Pre-Published version | 1.69 MB | Adobe PDF | View/Open |
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