为优化电动物流车充电需求预测方法,本文结合粒子群优化算法(PSO)和BP神经网络技术,对电动物流车的充电需求进行了系统性定量研究。首先,通过多元回归分析和主成分分析,从复杂多变的影响因素中提取关键变量,如车辆运行路径、电池容量、...为优化电动物流车充电需求预测方法,本文结合粒子群优化算法(PSO)和BP神经网络技术,对电动物流车的充电需求进行了系统性定量研究。首先,通过多元回归分析和主成分分析,从复杂多变的影响因素中提取关键变量,如车辆运行路径、电池容量、行驶距离和充电时间分布等。然后,基于提取的主要影响因素,构建了PSO-BP神经网络预测模型。根据预测的主要考虑因素,建立了PSO-BP神经网络预测模式。粒子群优化理论主要用来优化BP神经网络的初始权值和阈值,从而提高模型的预测准确度和减少了局部最优预测问题。通过实验验证,本研究模型在预测电动物流车充电需求方面表现出显著的精确性和可靠性。To optimize the prediction method of charging demand for electric logistics vehicles, this paper combines particle swarm optimization algorithm (PSO) and BP neural network technology to conduct a systematic quantitative study on the charging demand of electric logistics vehicles. Firstly, through multiple regression analysis and principal component analysis, key variables such as vehicle operating path, battery capacity, driving distance, and charging time distribution are extracted from complex and variable influencing factors. Then, based on the extracted main influencing factors, a PSO-BP neural network prediction model was constructed. Based on the main considerations of prediction, a PSO-BP neural network prediction mode was established. Particle swarm optimization theory is mainly used to optimize the initial weights and thresholds of BP neural networks, thereby improving the prediction accuracy of the model and reducing local optimal prediction problems. Through experimental verification, this research model demonstrates significant accuracy and reliability in predicting the charging demand of electric logistics vehicles.展开更多
文摘为优化电动物流车充电需求预测方法,本文结合粒子群优化算法(PSO)和BP神经网络技术,对电动物流车的充电需求进行了系统性定量研究。首先,通过多元回归分析和主成分分析,从复杂多变的影响因素中提取关键变量,如车辆运行路径、电池容量、行驶距离和充电时间分布等。然后,基于提取的主要影响因素,构建了PSO-BP神经网络预测模型。根据预测的主要考虑因素,建立了PSO-BP神经网络预测模式。粒子群优化理论主要用来优化BP神经网络的初始权值和阈值,从而提高模型的预测准确度和减少了局部最优预测问题。通过实验验证,本研究模型在预测电动物流车充电需求方面表现出显著的精确性和可靠性。To optimize the prediction method of charging demand for electric logistics vehicles, this paper combines particle swarm optimization algorithm (PSO) and BP neural network technology to conduct a systematic quantitative study on the charging demand of electric logistics vehicles. Firstly, through multiple regression analysis and principal component analysis, key variables such as vehicle operating path, battery capacity, driving distance, and charging time distribution are extracted from complex and variable influencing factors. Then, based on the extracted main influencing factors, a PSO-BP neural network prediction model was constructed. Based on the main considerations of prediction, a PSO-BP neural network prediction mode was established. Particle swarm optimization theory is mainly used to optimize the initial weights and thresholds of BP neural networks, thereby improving the prediction accuracy of the model and reducing local optimal prediction problems. Through experimental verification, this research model demonstrates significant accuracy and reliability in predicting the charging demand of electric logistics vehicles.
