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基于PSO-BP神经网络的新能源汽车销量预测模型

PSO-BP Neural Network Based New Energy VehicleSales Prediction Model
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摘要 为有效避免新能源汽车销量产销不平衡问题,通过粒子群优化算法(particle swarm optimization,PSO)优化反向传播(back propagation,BP)网络的参数迭代过程,弥补优化原本BP神经网络易陷入局部最优和收敛速度较慢的缺陷,构建了基于PSO-BP神经网络的新能源汽车销量预测模型,以比亚迪为例进行指数平滑法预测、BP和PSO-BP神经网络预测。结果表明BP神经网络模型相比于指数平滑模型在均方误差(mean square error,MSE)、平均绝对值误差(mean absolute error,MAE)和平均绝对百分比误差(mean absolute percentage error,MAPE)指标上预测性能优势显著,经过粒子群算法优化后的BP神经网络模型的MSE下降近7×107,MAE下降3346,MAPE下降1.71%。可见基于PSO-BP神经网络的新能源汽车销量预测模型优于指数平滑模型和BP神经网络模型,粒子群优化的BP神经网络能够使模型跳出局部最优,加快收敛速度,预测结果的误差率更低,精度更高,且对企业的计划和生产具有指导作用。 In order to effectively avoid the problem of imbalance between production and sales of new energy vehicles,the PSO algorithm was used to optimize the parameter iteration process of the BP neural network,to make up for the shortcomings of optimizing the original BP neural network which is easy to fall into the local optimum and slow convergence,a new energy vehicle sales prediction model based on the PSO-BP neural network was structructed,Taking BYD as an example the prediction of the exponential smoothing method,the BP and PSO-BP neural network was carried out.The prediction of BYD,BP and PSO-BP neural network were carried out.The results show that the BP neural network model has a significant advantage over the exponential smoothing model in terms of MSE,MAE and MAPE indicators,and the BP neural network model optimized by the particle swarm algorithm shows a decrease of nearly 7×107 in MSE,3346 in MAE and 1.71%in MAPE.It can be seen that the new energy vehicle sales prediction model based on PSO-BP neural network is better than the exponential smoothing model and BP neural network model,particle swarm optimized BP neural network can make the model jump out of the local optimum,accelerate the convergence speed,the prediction results of the error rate is lower,the accuracy is higher,and it has a guiding role in the planning and production of enterprises.
作者 王训洪 郝同铮 马聪 WANG Xun-hong;HAO Tong-zheng;MA Cong(School of Economics and Management,Guangxi University of Science and Technology,Liuzhou 545006,China;Guangxi Research Centre for High Quality Industrial Development,Liuzhou 545006,China)
出处 《科学技术与工程》 北大核心 2024年第31期13467-13474,共8页 Science Technology and Engineering
基金 国家自然科学基金(52074061) 广西科技基地与人才专项(桂科2022AC21084) 广西教育厅中青年教师科研基础能力提升项目(2024KY0347) 广西科技大学博士基金(校科博21S07,校科博23S04)。
关键词 新能源汽车 PSO算法 PSO-BP神经网络 销量预测模型 new energy vehicles PSO algorithm PSO-BP neural network sales forecast model
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