火电机组标煤耗率模型的变量选择方法
Selection Method of Variable of Standard Coal Consumption Rate Model of Thermal Power Generating Units
-
摘要: 变量选择是神经网络建模的基础,在火电机组中,影响标煤耗率的因素很多,如果将各种影响因素都包含进输入变量中,将造成相关的输入变量过多,加重神经网络的训练负担,增加陷入局部极小点的可能,降低神经网络的预测精度。文章首先分析了影响火电机组标煤耗率的因素,提出了一种基于敏感度分析的变量选择方法,然后采用该方法计算各个因素对输出的贡献率,并根据各个贡献率从众多影响因素中选取贡献最大的6个因素作为神经网络模型的输入变量。仿真结果表明,该变量选择方法简化了神经网络结构,减少了神经网络的训练时间,提高了神经网络的预测精度。Abstract: The variable selection is foundation in modeling of neural network.There are many influencing factors in thermal power generating units,which affect the coal consumption rate.If all sorts of influencing factors are included in input variable,related input variables will become overabundance,increase training burden of neural network,enhance possibility to get in local minimum point and reduce prediction precision of neural network.The paper firstly analyzed the factors that affected the standard coal consumption rate of thermal power generating units and proposed a method of variable selection based on sensitivity analysis,then it used the method to calculate contributing rate of each factor for output,and according to each contributing rate,the method selected six factors which had the most contributing rate from many influencing factors as input variable of neural network model.The simulation result showed that the method of variable selection could simplify structure of neural network,reduce training time of neural network and improve prediction precision.