Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN

被引:39
作者
Wu, Minfeng [1 ]
Chen, Wen [2 ]
机构
[1] Xiamen Univ Malaysia, Sch Elect Engn & Artificial Intelligence, Sepang 43900, Malaysia
[2] Jimei Univ, Coll Ocean Informat Engn, Xiamen 361021, Peoples R China
关键词
electrical vehicles; PCA; GRNN; artificial intelligence; CONSUMPTION;
D O I
10.3390/su14042206
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Since electric vehicles (EVs) could reduce the growing concerns on environmental pollution issues and relieve the social dependency of fossil fuels, the EVs market is fast increased in recent years. However, a large growth in the number of EVs will bring a great challenge to the present traffic system; thus, an acceptable model is necessary to forecast the sales of EVs in order to better plan the appropriate supply of necessary facilities (e.g., charging stations and sockets in car parks) as well as the electricity required on the road. In this study, we propose a model to predict the sales volume and increase rate of EVs in the world and China, using both statistics and machine learning methods by combining principle component analysis and a general regression neural network, based on the previous 11 years of sales data of EVs. The results indicate that a continuing growth in the sales of EVs will appear in both the world and China in the coming eight years, but the sales increase rate is slowly and continuously deceasing because of the persistent growth of the basic sales volume. The results also indicate that the increase rate of sales of EVs in China is higher than that of the world, and the proportion of sales of EVs in China will increase gradually and will be above 50% in 2025. In this case, large accessory facilities for EVs are required in China in the coming few years.
引用
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页数:14
相关论文
共 41 条
[1]   Local temperature forecasts based on statistical post-processing of numerical weather prediction data [J].
Alerskans, Emy ;
Kaas, Eigil .
METEOROLOGICAL APPLICATIONS, 2021, 28 (04)
[2]  
[Anonymous], 2021, Electric Vehicle Outlook 2021 BNEF
[3]   New grey forecasting model with its application and computer code [J].
Bilgil, Halis .
AIMS MATHEMATICS, 2021, 6 (02) :1497-1514
[4]   Forecasting Plug-In Electric Vehicle Sales and the Diurnal Recharging Load Curve [J].
Duan, Zhaoyang ;
Gutierrez, Brittni ;
Wang, Lizhi .
IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (01) :527-535
[5]   Effect of Battery Electric Vehicles on Greenhouse Gas Emissions in 29 European Union Countries [J].
Fuinhas, Jose Alberto ;
Koengkan, Matheus ;
Leitao, Nuno Carlos ;
Nwani, Chinazaekpere ;
Uzuner, Gizem ;
Dehdar, Fatemeh ;
Relva, Stefania ;
Peyerl, Drielli .
SUSTAINABILITY, 2021, 13 (24)
[6]   Life cycle CO2 footprint reduction comparison of hybrid and electric buses for bus transit networks [J].
Garcia, Antonio ;
Monsalve-Serrano, Javier ;
Sari, Rafael Lago ;
Tripathi, Shashwat .
APPLIED ENERGY, 2022, 308
[7]   A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN [J].
Ge, Leijiao ;
Xian, Yiming ;
Yan, Jun ;
Wang, Bo ;
Wang, Zhongguan .
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2020, 8 (06) :1268-1275
[8]   Modeling and Forecasting Electric Vehicle Consumption Profiles [J].
Gerossier, Alexis ;
Girard, Robin ;
Kariniotakis, George .
ENERGIES, 2019, 12 (07)
[9]   Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions [J].
Glerum, Aurelie ;
Stankovikj, Lidija ;
Themans, Michael ;
Bierlaire, Michel .
TRANSPORTATION SCIENCE, 2014, 48 (04) :483-499
[10]   What factors contribute to the mutual dependence degree of China in its crude oil trading relationship with oil-exporting countries? [J].
He, Shuying ;
Guo, Kun .
ENERGY, 2021, 228