A comparison study on electric vehicle growth forecasting based on grey system theory and NAR neural network

Xian Zhang, Ka Wing Chan, Xuesen Yang, Yangyang Zhou, Kexin Ye, Guibin Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Grey 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.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016
PublisherIEEE
Pages711-715
Number of pages5
ISBN (Electronic)9781509040759
ISBN (Print)9781509040759
DOIs
Publication statusPublished - 8 Dec 2016
Event7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 - Sydney, Australia
Duration: 6 Nov 20169 Nov 2016

Conference

Conference7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016
Country/TerritoryAustralia
CitySydney
Period6/11/169/11/16

Keywords

  • EV charging demand forecasting
  • grey system-forecasting theory
  • NAR neural network

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Control and Optimization
  • Signal Processing

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