Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/100622
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dc.contributorDepartment of Electrical and Electronic Engineering-
dc.creatorZhang, Xen_US
dc.creatorChan, KWen_US
dc.creatorYang, Xen_US
dc.creatorZhou, Yen_US
dc.creatorYe, Ken_US
dc.creatorWang, Gen_US
dc.date.accessioned2023-08-11T03:11:09Z-
dc.date.available2023-08-11T03:11:09Z-
dc.identifier.isbn978-1-5090-4075-9 (Electronic)en_US
dc.identifier.isbn978-1-5090-4076-6 (Print on Demand(PoD))en_US
dc.identifier.urihttp://hdl.handle.net/10397/100622-
dc.description2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), 06-09 November 2016, Sydney, NSW, Australiaen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights©2016 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.rightsThe following publication X. Zhang, K. W. Chan, X. Yang, Y. Zhou, K. Ye and G. Wang, "A comparison study on electric vehicle growth forecasting based on grey system theory and NAR neural network," 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2016, pp. 711-715 is available at https://doi.org/10.1109/SmartGridComm.2016.7778845.en_US
dc.subjectEV charging demand forecastingen_US
dc.subjectGrey system-forecasting theoryen_US
dc.subjectNAR neural networken_US
dc.titleA comparison study on electric vehicle growth forecasting based on grey system theory and NAR neural networken_US
dc.typeConference Paperen_US
dc.identifier.spage711en_US
dc.identifier.epage715en_US
dc.identifier.doi10.1109/SmartGridComm.2016.7778845en_US
dcterms.abstractGrey system forecasting theory model and nonlinear autoregressive (NAR) neural network model for forecasting the number of electric vehicles (EVs) in the city of Shenzhen are established in this paper separately. The number of EVs from 2006 to 2015 was used as the raw data in two models. The effectiveness of the two models are evaluated by various criteria. Afterward, the rationality, precision and the adaptability of the two models are compared. At last, the better model was used to forecasting the number of EVs in Shenzhen from 2016 to 2020.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn Proceedings of 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), 06-09 November 2016, Sydney, NSW, Australia, 2016, p. 711-715en_US
dcterms.issued2016-
dc.identifier.scopus2-s2.0-85010197229-
dc.relation.conferenceIEEE International Conference on Smart Grid Communications [SmartGridComm]-
dc.description.validate202308 bckw-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberEE-0622-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe Hong Kong Polytechnic University Research Studentship; Shenzhen University Research and Development Startup Fund; National Basic Research Program (973 Program); National Natural Science Foundation of China; Natural Science Foundation of Guangdong Provinceen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS9586740-
dc.description.oaCategoryGreen (AAM)en_US
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