Urban NEV sales in China.
<div><p>To accurately predict the sales of new energy vehicles (NEVs) in Chinese cities and explore the applicability of optimization algorithms for GRU models in forecasting urban NEV sales., this paper conducts a spatiotemporal analysis of urban NEV sales data. The Whale Optimization A...
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| منشور في: |
2025
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| _version_ | 1852021183497633792 |
|---|---|
| author | Xiangtu Li (21156158) |
| author_facet | Xiangtu Li (21156158) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xiangtu Li (21156158) |
| dc.date.none.fl_str_mv | 2025-04-21T17:41:12Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320962.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Urban_NEV_sales_in_China_/28833104 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified yangtze river delta pearl river delta offering critical insights new energy vehicles mean absolute error urban areas using particle swarm optimization overall automobile sales monthly sales forecasting monthly sales prediction whale optimization algorithm higher nev sales bigru model outperforms prediction results optimization techniques optimization algorithms nev sales level sales xlink "> urban nevs thereby proposing standalone bigru spatiotemporal analysis research findings predominantly concentrated predicting city power grid paper conducts nev industry hebei region gru models emission reduction declining trend charging infrastructure bigru model based model accurately predict 72 lower 18 lower |
| dc.title.none.fl_str_mv | Urban NEV sales in China. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>To accurately predict the sales of new energy vehicles (NEVs) in Chinese cities and explore the applicability of optimization algorithms for GRU models in forecasting urban NEV sales., this paper conducts a spatiotemporal analysis of urban NEV sales data. The Whale Optimization Algorithm (WOA) is then employed to optimize the parameters of the Bidirectional Gated Recurrent Unit (BiGRU) model, thereby proposing a WOA-BiGRU-based model for monthly sales prediction for urban NEVs. Its prediction results are compared with those of the particle swarm optimization (PSO) algorithm. The research findings are as follows: The growth of NEV sales has reversed the declining trend of overall automobile sales in China; Cities with higher NEV sales are predominantly concentrated in four major economic hubs--the Pearl River Delta, Yangtze River Delta, Beijing-Tianjin-Hebei region, and Chengdu-Chongqing. Optimization techniques such as WOA can improve the accuracy of GRU models in predicting city-level sales of NEV. The WOA-BiGRU model outperforms both the standalone BiGRU and PSO models, achieving a Mean Absolute Error (MAE) of 3051.89, which is 526.18 lower than the BiGRU model and 104.72 lower than that of the PSO model. This study improves the accuracy of monthly sales prediction for urban NEVs, offering critical insights for the development of the NEV industry in China, the deployment of charging infrastructure, the stabilization of the power grid, and emission reduction in the transportation sector.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_45235f70f528e9cfdcf000c00a247ebb |
| identifier_str_mv | 10.1371/journal.pone.0320962.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28833104 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Urban NEV sales in China.Xiangtu Li (21156158)EcologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedyangtze river deltapearl river deltaoffering critical insightsnew energy vehiclesmean absolute errorurban areas usingparticle swarm optimizationoverall automobile salesmonthly sales forecastingmonthly sales predictionwhale optimization algorithmhigher nev salesbigru model outperformsprediction resultsoptimization techniquesoptimization algorithmsnev saleslevel salesxlink ">urban nevsthereby proposingstandalone bigruspatiotemporal analysisresearch findingspredominantly concentratedpredicting citypower gridpaper conductsnev industryhebei regiongru modelsemission reductiondeclining trendcharging infrastructurebigru modelbased modelaccurately predict72 lower18 lower<div><p>To accurately predict the sales of new energy vehicles (NEVs) in Chinese cities and explore the applicability of optimization algorithms for GRU models in forecasting urban NEV sales., this paper conducts a spatiotemporal analysis of urban NEV sales data. The Whale Optimization Algorithm (WOA) is then employed to optimize the parameters of the Bidirectional Gated Recurrent Unit (BiGRU) model, thereby proposing a WOA-BiGRU-based model for monthly sales prediction for urban NEVs. Its prediction results are compared with those of the particle swarm optimization (PSO) algorithm. The research findings are as follows: The growth of NEV sales has reversed the declining trend of overall automobile sales in China; Cities with higher NEV sales are predominantly concentrated in four major economic hubs--the Pearl River Delta, Yangtze River Delta, Beijing-Tianjin-Hebei region, and Chengdu-Chongqing. Optimization techniques such as WOA can improve the accuracy of GRU models in predicting city-level sales of NEV. The WOA-BiGRU model outperforms both the standalone BiGRU and PSO models, achieving a Mean Absolute Error (MAE) of 3051.89, which is 526.18 lower than the BiGRU model and 104.72 lower than that of the PSO model. This study improves the accuracy of monthly sales prediction for urban NEVs, offering critical insights for the development of the NEV industry in China, the deployment of charging infrastructure, the stabilization of the power grid, and emission reduction in the transportation sector.</p></div>2025-04-21T17:41:12ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0320962.g003https://figshare.com/articles/figure/Urban_NEV_sales_in_China_/28833104CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/288331042025-04-21T17:41:12Z |
| spellingShingle | Urban NEV sales in China. Xiangtu Li (21156158) Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified yangtze river delta pearl river delta offering critical insights new energy vehicles mean absolute error urban areas using particle swarm optimization overall automobile sales monthly sales forecasting monthly sales prediction whale optimization algorithm higher nev sales bigru model outperforms prediction results optimization techniques optimization algorithms nev sales level sales xlink "> urban nevs thereby proposing standalone bigru spatiotemporal analysis research findings predominantly concentrated predicting city power grid paper conducts nev industry hebei region gru models emission reduction declining trend charging infrastructure bigru model based model accurately predict 72 lower 18 lower |
| status_str | publishedVersion |
| title | Urban NEV sales in China. |
| title_full | Urban NEV sales in China. |
| title_fullStr | Urban NEV sales in China. |
| title_full_unstemmed | Urban NEV sales in China. |
| title_short | Urban NEV sales in China. |
| title_sort | Urban NEV sales in China. |
| topic | Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified yangtze river delta pearl river delta offering critical insights new energy vehicles mean absolute error urban areas using particle swarm optimization overall automobile sales monthly sales forecasting monthly sales prediction whale optimization algorithm higher nev sales bigru model outperforms prediction results optimization techniques optimization algorithms nev sales level sales xlink "> urban nevs thereby proposing standalone bigru spatiotemporal analysis research findings predominantly concentrated predicting city power grid paper conducts nev industry hebei region gru models emission reduction declining trend charging infrastructure bigru model based model accurately predict 72 lower 18 lower |