Prospect and sustainability prediction of China's new energy vehicles sales considering temporal and spatial dimensions

Ning, Taiyu; Lu, Bingquan; Ouyang, Xinyu; Ouyang, Hongwu; Chen, Jiayi

doi:10.1016/j.jclepro.2024.142926

Journal of Cleaner Production

2024

DOI: 10.1016/j.jclepro.2024.142926

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Prospect and sustainability prediction of China's new energy vehicles sales considering temporal and spatial dimensions

Taiyu Ning,

Bingquan Lu,

Xinyu Ouyang

et al.

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2024

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Cited by 2 publications

References 35 publications

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New Energy Vehicle Development and Electricity Demand Forecasting Based on Random Forest Model

Zhou,

Wang,

Zhang

2024

E3S Web Conf.

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With the implementation of the green economy and the decarbonization strategy, the new energy automobile industry has developed rapidly in China, which poses new challenges to the balance and stability of the power system. This paper predicts the development trend of China's new energy vehicle industry through the random forest model, and analyses the impact of the development of new energy vehicles on power demand. The results show that the number of new energy vehicles in China is expected to increase significantly, accounting for a quarter of the total number of vehicles, and the number of charging piles will increase significantly to meet the demand. With the development of the new energy automobile industry, the demand for electricity and power load in the whole society are expected to maintain rapid growth, which poses new challenges to the power supply stability of the power grid. This study provides an important reference for government regulation, power grid adaptation and new energy vehicle enterprise development planning.

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New Energy Vehicle Development and Electricity Demand Forecasting Based on Random Forest Model

Zhou,

Wang,

Zhang

2024

E3S Web Conf.

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Deep Learning Forecasting Model for Market Demand of Electric Vehicles

Simsek,

Koç,

Desticioglu Tasdemir

et al. 2024

Applied Sciences

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The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these considerations, this study presents an innovative methodology for forecasting EV demand. This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. Experimental research has investigated four different categories of electric vehicles: battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and all electric vehicles (ALL). Performance measures were calculated after conducting experimental studies to assess the model’s ability to predict electric vehicle demand. When the performance measures (mean absolute error, root mean square error, mean squared error, R-Squared) of EVs-PredNet and machine learning regression methods are compared, the proposed model is more effective than the other forecasting methods. The experimental results demonstrate the effectiveness of the proposed approach in forecasting the electric vehicle demand. This model is considered to have significant application potential in assessing the adoption and demand of electric vehicles. This study aims to improve the reliability of forecasting future demand in the electric vehicle market and to develop relevant approaches.

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Prospect and sustainability prediction of China's new energy vehicles sales considering temporal and spatial dimensions

Cited by 2 publications

References 35 publications

New Energy Vehicle Development and Electricity Demand Forecasting Based on Random Forest Model

New Energy Vehicle Development and Electricity Demand Forecasting Based on Random Forest Model

Deep Learning Forecasting Model for Market Demand of Electric Vehicles

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