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 language | English |
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Title of host publication | 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 |
Publisher | IEEE |
Pages | 711-715 |
Number of pages | 5 |
ISBN (Electronic) | 9781509040759 |
ISBN (Print) | 9781509040759 |
DOIs | |
Publication status | Published - 8 Dec 2016 |
Event | 7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 - Sydney, Australia Duration: 6 Nov 2016 → 9 Nov 2016 |
Conference
Conference | 7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 |
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Country/Territory | Australia |
City | Sydney |
Period | 6/11/16 → 9/11/16 |
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