基于神经网络的运氢需求预测模型研究

Research on Hydrogen Demand Prediction Model Based on Neural Network

本文提出了一种基于神经网络的时间序列预测模型,能够有效地利用预测运氢需求与时间的关系,提高了预测的精度和稳定性。通过与几种经典的时间序列预测模型进行比较,结果表明,该模型在所有评价指标上都优于其他模型;同时对比了一个月内运氢需求的真实值和预测值,变化趋势和波动程度都拟合较好,表明该模型的有效性和可靠性。

A neural network-based time series forecasting model is proposed in this paper,which can effectively use the relationship between the hydrogen demand and time to improve the accuracy and stability of the prediction.By comparing with several classic time series forecasting models,the results show that the proposed model outperforms other models on all evaluation indicators;meanwhile,the real and predicted values of hydrogen demand within a month are compared,and the change trend and fluctuation degree are wellfitted,indicating the effectiveness and reliability of the model.