A study on monthly sales forecasting of new energy vehicles in urban areas using the WOA-BiGRU model DOI Creative Commons

X. Li

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0320962 - e0320962

Published: April 21, 2025

To accurately predict the sales of new energy vehicles (NEVs) in Chinese cities and explore applicability optimization algorithms for GRU models forecasting urban NEV sales., this paper conducts a spatiotemporal analysis data. The Whale Optimization Algorithm (WOA) is then employed to optimize parameters Bidirectional Gated Recurrent Unit (BiGRU) model, thereby proposing WOA-BiGRU-based model monthly prediction NEVs. Its results are compared with those particle swarm (PSO) algorithm. research findings as follows: growth has reversed declining trend overall automobile China; Cities higher predominantly concentrated four major economic hubs--the Pearl River Delta, Yangtze Beijing-Tianjin-Hebei region, Chengdu-Chongqing. techniques such WOA can improve accuracy predicting city-level NEV. WOA-BiGRU outperforms both standalone BiGRU PSO models, achieving Mean Absolute Error (MAE) 3051.89, which 526.18 lower than 104.72 that model. This study improves NEVs, offering critical insights development industry China, deployment charging infrastructure, stabilization power grid, emission reduction transportation sector.

Language: Английский

A study on monthly sales forecasting of new energy vehicles in urban areas using the WOA-BiGRU model DOI Creative Commons

X. Li

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0320962 - e0320962

Published: April 21, 2025

To accurately predict the sales of new energy vehicles (NEVs) in Chinese cities and explore applicability optimization algorithms for GRU models forecasting urban NEV sales., this paper conducts a spatiotemporal analysis data. The Whale Optimization Algorithm (WOA) is then employed to optimize parameters Bidirectional Gated Recurrent Unit (BiGRU) model, thereby proposing WOA-BiGRU-based model monthly prediction NEVs. Its results are compared with those particle swarm (PSO) algorithm. research findings as follows: growth has reversed declining trend overall automobile China; Cities higher predominantly concentrated four major economic hubs--the Pearl River Delta, Yangtze Beijing-Tianjin-Hebei region, Chengdu-Chongqing. techniques such WOA can improve accuracy predicting city-level NEV. WOA-BiGRU outperforms both standalone BiGRU PSO models, achieving Mean Absolute Error (MAE) 3051.89, which 526.18 lower than 104.72 that model. This study improves NEVs, offering critical insights development industry China, deployment charging infrastructure, stabilization power grid, emission reduction transportation sector.

Language: Английский

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