自回归神经网络的预测值反馈再训练策略及应用  

Predictive value feedback retraining strategy and application for autoregressive neural networks

作　　者：莫正阳 李益国[1] Mo Zhengyang;Li Yiguo(National Engineering Research Center of Power Generation Control and Safety,Southeast University,Nanjing 210096,China)

机构地区：[1]东南大学大型发电装备安全运行与智能测控国家工程研究中心,南京210096

出　　处：《东南大学学报（自然科学版）》2024年第3期738-746,共9页Journal of Southeast University：Natural Science Edition

基　　金：国家自然科学基金资助项目(52076038)。

摘　　要：为提高非线性自回归神经网络(NARX-NN)的多步预测性能,提出了一种预测值反馈再训练(FR)策略.首先采用常规训练策略对NARX-NN进行训练,然后利用模型的单步预测结果替换实测值,得到重构训练集,并指导网络再次训练.为验证FR的有效性,将其应用于3种典型的NARX-NN模型:非线性自回归深度神经网络(NARX-DNN)、基于长短期记忆网络的编码器-解码器(LSTMED)和深度自回归网络(DeepAR),以预测燃煤锅炉NO_(x)质量浓度或综合能源系统电负荷.与常规训练策略和计划采样的对比结果表明,采用FR的NARX-NN具有最高的多步预测精度,其中,LSTMED对NO_(x)质量浓度前向15步预测的平均绝对百分比误差(MAPE)为4.01%;DeepAR对电负荷前向24步预测的平均MAPE为4.34%.配对样本T检验结果表明,FR对NARX-NN的多步预测性能提升具有显著性.通过保持训练阶段和预测阶段输入的一致性,FR有效提升了NARX-NN模型的多步预测精度.To improve the multi-step prediction performance of nonlinear autoregressive neural network(NARX-NN),a predictive value feedback retraining(FR)strategy was proposed.Initially,the NARX-NN was trained using conventional training strategies.Then,the training samples were reconstructed by replacing the measured values with the one-step predicted values,which were used to train the network again.To validate the effectiveness of FR,it was applied to three typical NARX-NN models:nonlinear autoregressive deep neural network(NARX-DNN),encoder-decoder based on long short-term memory network(LSTMED)and deep autoregressive network(DeepAR)for predicting the NO_(x)mass concentration of coal-fired boilers or the electrical load of integrated energy system.Comparison results with conventional training strategies and scheduled sampling show that NARX-NN with FR has the highest multi-step prediction accuracy,with a mean absolute percentage error(MAPE)of 4.01%for LSTMED for 15-step forward prediction of NO_(x)mass concentration and 4.34%for DeepAR for 24-step forward prediction of electrical loads.The results of paired-sample T-test indicate that FR improves the multi-step prediction performance of NARX-NN significantly.By keeping the consistency of the inputs in the training and prediction phases,FR effectively improves the multi-step prediction accuracy of the NARX-NN model.

关 键 词：神经网络 多步预测 训练策略 NO_(x)质量浓度 电负荷 

分 类 号：TK3[动力工程及工程热物理—热能工程] TP183[自动化与计算机技术—控制理论与控制工程]