Research on Sales of New Energy Vehicles Based on ARIMA Model
DOI:
https://doi.org/10.54097/18wn5a82Keywords:
Spearman Correlation Analysis, ARIMA, BP Neural Network, Entropy Weight TOPSIS Evaluation.Abstract
Firstly, this article identifies seven indicators based on electric vehicles: battery endurance, number of public charging facilities, government subsidies, gasoline prices, electricity prices, carbon emissions, and number of power battery companies. Spearman correlation analysis is conducted between indicator data and sales of new energy electric vehicles. The correlation coefficients of the first five indicators are all greater than 0.6, which is the main influencing factor. Then, the sales of new energy vehicles are predicted using a yearly forecasting method. Due to the close correlation between the sales of new energy vehicles and the five main indicators, the ARIMA time series model is first used to predict the annual quantity of the five indicators. Then, using these five indicators as inputs and sales as outputs, a BP neural network model is established to predict the annual sales of new energy vehicles in China to be 46051748 units in ten years. Finally, quantifying the score of ecological environment assessment using four evaluation indicators: sulfur dioxide, smoke emissions, ammonia nitrogen, and carbon dioxide. The entropy weight method was used to determine the weights of each indicator, and the results were 37.25%, 30.96%, 21.24%, and 10.55%, respectively. Then apply it to the TOPSIS model to obtain ecological environment scores for different years, and analyze the correlation between the degree of electrification and ecological environment scores. Finally, it can be concluded that the larger the market share of new energy vehicles, the better the ecological environment.
Downloads
References
[1] Wang Li; Cao Shiyi; Song Kaifa; Huang Jigui Application of ARIMA model in predicting hand, foot, and mouth disease epidemic in Jingzhou City [J]. Chinese Journal of Social Medicine,2023,40(05):623-626.
[2] Zhang Yong. Evaluation of water resource carrying capacity of Xingfu River Lake in Anhui Province based on entropy weight method and TOPSIS model [J]. Shaanxi Water Resources,2023,(09):45-47.
[3] Fan Cairui; Zheng Hui; Zhang Wenying; Ren Xinmei. Research on the Health Evaluation Index System of Daihai Lake Based on AHP Entropy Weight Method [J]. Inner Mongolia Water Conservancy,2023,(07):9- 11.
[4] He Xiaolong. Research on Comprehensive Data Quality Evaluation of Power Dispatching Based on TOPSIS Method [J]. Electrical Application,2023,42(10):55-61.
[5] Lian Qiang. Spearman rank correlation coefficient of comprehensive interval numbers and its application [J]. Journal of Chongqing University of Technology (Natural Science Edition),2020,37(06):71-75.
[6] Zhou Li; Sun Xiaohan. Evaluation of Water Resources Carrying Capacity in Zhejiang Province Based on BP Neural Network [J]. Journal of Wenzhou University (Natural Science Edition),2023,44(04):25-31.
[7] Xing Yuqi; Zhou Jia; Huang Weili; Wang Junjuan; Liu Meiyan; Hu Xiaoping. A prediction model for wheat scab in rice wheat rotation areas based on BP neural network [J]. Northwest Agricultural Journal,2023,32(11):1842- 1848.
[8] Teng is steady and steady; Zhang et al. Research on Risk Control of Logistics Supply Chain Finance Based on Entropy Weight Method [J]. Times Economics and Trade, 2023,20 (10): 60-64.
[9] Zheng S , Huang J H .New Energy Vehicles Sales Prediction Method and Empirical Research Under the Environment of Big Data[J]. 2018.
[10] Da-Hai Z , Shi-Fang J , Yan-Qiu B I ,et al.Study of power system load forecast based on wavelet neural networks[J].Electric Power Automation Equipment, 2003.
Downloads
Published
Conference Proceedings Volume
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.