基于GWO-VMD-LSTM的热力系统单参数时间序列预测方法
Time Sequential Prediction Method of Single Parameter for Thermal System based on GWO-VMD-LSTM
卓越 1倪何 1肖鹏飞2
作者信息
- 1. 海军工程大学动力工程学院,湖北武汉 430033
- 2. 中国船舶集团有限公司第七○三研究所,黑龙江哈尔滨 150078
- 折叠
摘要
针对热力系统参数运行数据预测困难、准确率低的问题,基于灰狼算法(Grey Wolf Optimizer,GWO)、变分模态分解(Variational Mode Decomposition,VMD)、长短期记忆模型(Long Short Term Memory,LSTM)提出一种单参数时序预测方法.首先,使用改进适应度函数的GWO对VMD的分解层数和惩罚系数进行寻优;其次,以最优参数对运行数据进行VMD,并将筛选出的本征模态函数(intrinsic mode function,IMF)分量作为原始数据趋势项;最后,以此运行参数趋势项作为LSTM的训练集输入特征向量,构建LSTM,LSTM超参数由北方苍鹰算法(Northern Goshawk Optimization,NGO)得到.经实际案例验证,该方法可以通过降低原始数据的噪声和扰动对LSTM的影响,增加LSTM对热力参数运行趋势的可预测时间长度和预测精度,相较于传统的LSTM,所提方法的有效预测时间长度增加约176％、预测精度提高约158％.
Abstract
It is difficult to predict the operation data of thermal system parameters,and the accuracy of prediction data is low.Based on grey wolf optimizer(GWO)algorithm,variational mode decomposition(VMD)and long short term memory(LSTM)model,a time sequential prediction method of single pa-rameter is proposed.Firstly,the number of decomposition layers and penalty coefficients of VMD are op-timized by GWO with improved fitness function;secondly,VMD of operation data is conducted with opti-mal parameters and the intrinsic mode function(IMF)components are screened out as the original data trend term;finally,the LSTM is constructed by using the running parameter trend term as the input fea-ture vector of the training set which is used for constructing LSTM,and the LSTM hyperparameters are obtained by the northern goshawk optimization(NGO)algorithm.Verified by case studies,this method effectively mitigates the impact of noise and disturbances in the original data on LSTM,thereby enhancing its predictive time length and accuracy for thermal parameter trends.In comparison to traditional LSTM models,the proposed method demonstrates a remarkable 176％increase in effective prediction time length and a substantial 158％improvement in prediction accuracy.
关键词
时序预测/灰狼算法/模态分解/长短期记忆网络Key words
time sequential prediction/grey wolf algorithm(GWO)/mode decomposition/long short term memory(LSTM)network引用本文复制引用
基金项目
国家自然科学基金面上项目(51909254)
海军工程大学自主研发基金资助项目(425317T014)
出版年
2024
NETL
NSTL
万方数据