基于改进灰狼算法优化双向长短时记忆神经网络的水冷壁壁温预测
Prediction of water wall temperature based on improved grey wolf optimizer and bidirectional long and short term memory network
詹毅 1冯磊华 1杨锋 2钟信1
作者信息
- 1. 长沙理工大学能源与动力工程学院,湖南 长沙 410114
- 2. 华自科技股份有限公司,湖南 长沙 410006
- 折叠
摘要
提出一种基于改进灰狼(MGWO)算法优化双向长短时记忆(BiLSTM)神经网络的水冷壁壁温预测模型,灰狼算法采用非线性因子调整策略、自适应位置更新策略和动态权重修改策略进行改进以提升算法的全局寻优能力,利用改进灰狼算法优化BiLSTM模型的隐藏层数量、学习率和正则化参数以提高模型的预测精度,采用新疆某电厂的数据进行预测仿真,结果表明:改进后的算法预测精度更高,在机组升、降负荷时,均可以预测到壁温的变化趋势,模型的平均均方根误差相比于长短时记忆(LSTM)神经网络、BiLSTM模型分别降低了 9.86％和3.69％,且可以提前预测到水冷壁壁温的超温情况,对于预防水冷壁超温有重要意义.
Abstract
An improved grey wolf optimizer(MGWO)is used to optimize BiLSTM to predict water wall temperature.The improved algorithm adopts nonlinear factor adjustment strategy,adaptive position update strategy and dynamic weight modification strategy to improve the global optimization ability of the GWO.The improved grey wolf optimizer is used to optimize the number of hidden layers,learning rate and regularization parameters of the BiLSTM model to improve the prediction accuracy of the model.The data of a power plant in Xinjiang are used for prediction simulation.The results show that,the improved optimizer has higher prediction accuracy,and can predict the change trend of wall temperature when the unit is lifting and lowering load.Compared with the LSTM and BiLSTM models,the average root mean square error of the model reduces by 9.86％and 3.69％,respectively,and the overtemperature of water wall temperature can be predicted in advance,which is of great significance for the prevention of overtemperature of water wall.
关键词
水冷壁/壁温预测/双向长短时记忆神经网络/改进灰狼算法/自适应位置更新Key words
water wall/prediction of wall temperature/bidirectional long and short term memory neural network/improved grey wolf optimizer/adaptive location updates引用本文复制引用
基金项目
湖南省自然科学基金(2018JJ3552)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据