Abstract
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.
Highlights
The continuously augmented threat of global climate change, environment deterioration, and resource absence due to the increasing consumption of non-renewable fossil fuels has been a world-wide problem, which needs to be resolved in the near future for human sustainable development
We summarize the sales of electric vehicles (EVs) in the recent 11 years from 2010 to 2020, which are, We input summarize the sales
The sales of EVs in China have been forecasted by a model combining Principle component analysis (PCA) and general regression neural network (GRNN)
Summary
The continuously augmented threat of global climate change, environment deterioration, and resource absence due to the increasing consumption of non-renewable fossil fuels has been a world-wide problem, which needs to be resolved in the near future for human sustainable development. The electricity demand will cause a momentous increase in road power stations, which is not set up well currently In this case, it is necessary to forecast the sales volume of EVs, which would be accomplished by using reference data for a city or country to subsequently build up the supporting infrastructure for the popularization of EVs. In recent years, mainly two approaches have been utilized to forecast the future amounts of products (such as EVs and their charging loads [18,19]), i.e., statistical and machine learning methods. After the original data are decomposed, the amount of information data becomes very huge with all of the sub-bands, and the dimension of the feature space is too high, which results in a large amount of computation For this case, a reasonable route of data dimension reduction with minimizing the loss of the original data information needs to be set up [27].
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