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计量学报  2021, Vol. 42 Issue (5): 675-680    DOI: 10.3969/j.issn.1000-1158.2021.05.20
  电离辐射、标准物质与生物计量 本期目录 | 过刊浏览 | 高级检索 |
基于互信息PSO-LSSVM的SO2浓度预测
金秀章,李京
华北电力大学 控制与计算机工程学院,河北 保定 071003
Prediction of SO2 Concentration Based on Mutual Information PSO-LSSVM
JIN Xiu-zhang,LI Jing
School of Control and Coputer Engineering, North China Electric Power University, Baoding, Hebei 071003, China
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摘要 针对火电厂SO2污染物排放问题,提出了一种基于互信息的粒子群寻优(PSO)最小二乘支持向量机(LSSVM)模型预测方法,通过筛选出与SO2实测入口浓度相关性较高的辅助变量,将其作为模型的输入,实现对主导变量SO2浓度的预测。利用互信息筛选出的辅助变量相比于机理分析、皮尔逊相关性筛选出的辅助变量具有更高的相关性。利用互信息筛选出的辅助变量作为LSSVM模型的输入以及粒子群法确定LSSVM的参数,不仅缩短了计算时间,还提高了预测精度。将该方法应用到某火电厂的SO2浓度软测量中,利用现场数据进行仿真,结果表明预测精度较高,相对误差较低,预测趋势更贴近实际值,减小了实际值与预测值的误差(均方根误差为2.485,平均相对误差为0.2603%),为现场的SO2浓度提前控制提供了软件技术支持。
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金秀章
李京
关键词 计量学SO2浓度预测互信息粒子群寻优最小二乘支持向量机最小冗余最大相关性    
Abstract:Aiming at the problem of SO2 pollutant emission in thermal power plant, a PSO-LSSVM model prediction method was proposed based on the mutual information.The auxiliary variables of high correlation with the measured inlet concentration of SO2 were selected as the input of the model to realize the prediction of the dominant variable SO2 concentration.The auxiliary variables screened by mutual information had the higher correlation with the auxiliary variables selected by mechanism analysis and Pearson correlation. The auxiliary variables selected by mutual information were used as the input of the LSSVM model and the particle swarm optimization (PSO) was used to determine the parameters of the LSSVM not only reduced the calculation time, but also improved the prediction accuracy. The method was applied to the soft measurement of SO2 concentration in a thermal power plant, and the simulation was carried out by using field data, which showed that the prediction accuracy was higher, the relative error was lower, the prediction trend was closer to the actual value, and the error between the actual value and the predicted value was reduced (the square root error is 2.485 and the average relative error is 0.2603%). It provided the software technical support for the on-site SO2 concentration advance control.
Key wordsmetrology    SO2 concentration prediction    mutual information    PSO    LSSVM    mRMR
收稿日期: 2019-10-15      发布日期: 2021-05-24
PACS:  TB99  
通讯作者: 李京(1995-),男,山东莱芜人,华北电力大学硕士研究生,主要研究方向为机器学习、模型预测、火电厂机组脱硫脱硝等。Email: 837026561@qq.com     E-mail: 837026561@qq.com
作者简介: 金秀章(1969-),男,河北衡水人,华北电力大学副教授,主要从事先进控制策略在大型电力机组的应用、信息融合技术等方面的研究。Email: jinxzsys@163.com
引用本文:   
金秀章,李京. 基于互信息PSO-LSSVM的SO2浓度预测[J]. 计量学报, 2021, 42(5): 675-680.
JIN Xiu-zhang,LI Jing. Prediction of SO2 Concentration Based on Mutual Information PSO-LSSVM. Acta Metrologica Sinica, 2021, 42(5): 675-680.
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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2021.05.20     或     http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2021/V42/I5/675
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