摘要
针对火力发电厂存煤量预测精度不高的问题,提出了一种基于奇异谱分析(SSA)和长短时记忆(LSTM)神经网络的多变量多步预测模型.考虑电厂历史数据和温度的影响,以奇异谱分析或小波分析(wavelet analysis)对数据做平滑处理,剔除数据中的噪声成分,作为LSTM神经网络的输入向量进行仿真测试.结果表明,相比于普通BP(back propagation)神经网络、循环神经网络(RNN)和小波分析与LSTM结合算法,本文采用的基于奇异谱分析和长短时记忆神经网络的多变量多步预测模型精度更高.
In view of the low accuracy of coal storage predict,ion in thermal power plants,we propose a multi-variable and multi-step prediction model based on singular spectrum analysis( SSA) and long short-term memory( LSTM) neural network. Considering the influence of historical data and temperature of power plant,we use singular spectrum analysis or wavelet analysis to smooth the data,remove the noise components from the data,and use it as the input vectors of LSTM neural network for simulation test. The results show that compared with the combination of back propagation( BP) neural network,recurrent neural network,wavelet analysis and LSTM,the multi variable and multi-step prediction model based on singular spectrum analysis and long short time memory neural network adopted has higher accuracy.
作者
孔雯
车权
赵慧荣
彭道刚
KONG Wen;CHE Quan;ZHAO Huirong;PENG Daogang(School of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Chongqing Electric Power Company of State Grid,Chongqing 400014,China)
出处
《信息与控制》
CSCD
北大核心
2020年第6期742-751,共10页
Information and Control
基金
上海市“科技创新行动计划”高新技术领域资助项目(19511101600)
上海市科学技术委员会工程技术研究中心资助项目(14DZ2251100)
国网重庆市电力公司科技资助项目(2019渝电科技15#)。
关键词
火电厂存煤量
短期预测
奇异谱分析
多变量多步预测
长短时记忆神经网络
coal storage in thermal power plant
short-term prediction
singular spectrum analysis
multi-variable and multi-step prediction
long short-term memory neural network
