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<p>Carbon neutrality is facilitated by new energy vehicles, and their forecast and analysis aid in anticipating future development trends and enabling strategic deployment beforehand. The paper builds a new hybrid model using a BP neural network and a weakened variable weight buffer operator. The sales volume of plug-in hybrid vehicles and the total sales volume of new energy vehicles are forecasted using the suggested methodology in order to confirm the efficacy of prediction accuracy. The findings demonstrate that the suggested model outperforms the GM(1,1) model, buffer operator GM(1,1) model, and residual-modified GM(1,1) model in terms of prediction performance. This suggests that the BP neural network and buffer operator are useful for enhancing the GM(1,1) model's prediction accuracy.</p>

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Synergistic Optimization of GM(1,1) Model with Buffer Operators and Residual Correction and its Applications

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Chaofeng Shen,
  • Jun Zhang

摘要

Carbon neutrality is facilitated by new energy vehicles, and their forecast and analysis aid in anticipating future development trends and enabling strategic deployment beforehand. The paper builds a new hybrid model using a BP neural network and a weakened variable weight buffer operator. The sales volume of plug-in hybrid vehicles and the total sales volume of new energy vehicles are forecasted using the suggested methodology in order to confirm the efficacy of prediction accuracy. The findings demonstrate that the suggested model outperforms the GM(1,1) model, buffer operator GM(1,1) model, and residual-modified GM(1,1) model in terms of prediction performance. This suggests that the BP neural network and buffer operator are useful for enhancing the GM(1,1) model's prediction accuracy.