Demand and supply gap analysis of Chinese new energy vehicle charging infrastructure: Based on CNN-LSTM prediction model

计算机科学 卷积神经网络 供求关系 人工神经网络 需求预测 环境经济学 人工智能 运筹学 工程类 电气工程 经济 微观经济学
作者
Baozhu Li,Xiaotian Lv,Jiaxin Chen
出处
期刊:Renewable Energy [Elsevier BV]
卷期号:220: 119618-119618 被引量:21
标识
DOI:10.1016/j.renene.2023.119618
摘要

The sales of new energy vehicles (NEVs) and the construction of charging infrastructure promote and constrain each other. It is crucial for the development of the new energy vehicle industry to understand the gap clearly and accurately between the supply and demand of NEV charging infrastructure. In this paper, a neural network combined model based on convolutional neural network (CNN) and long and short-term memory (LSTM) is introduced for accurate prediction of NEVS sales and charging infrastructure ownership. Compared with other traditional and combined models, the CNN-LSTM combined model performs best in multiple evaluation metrics while using less computing power. The RMSE, MAE, MAPE, and R2 of the CNN-LSTM combined model were 52.80, 42.67, 17 %, and 0.78, respectively. Accordingly, it is sufficient to demonstrate the excellent prediction performance of the CNN-LSTM combined model constructed in this paper. The forecast results show that in 2025, the ratio of NEVs to public charging piles will rise to 10.2:1 and the ratio to private charging piles will fall to 2.5:1. The overall ratio shows a downward trend and is expected to reach 2:1. There is a gap in the demand for NEV charging infrastructure. Finally, this paper makes suggestions for narrowing the gap between the supply and demand of NEV charging infrastructure and the sustainable development of the NEV industry.
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