摘要
随着机器学习的发展,神经网络技术为燃煤电站中海量数据的分析与预测提供了一种解决手段。为方便电网更好地进行电力资源的调度,机组出力预测至关重要。通过BP神经网络对某300 MW循环流化床机组进行建模,并通过数据预处理减少异常数据对模型精确度的影响,通过主成分分析法(PCA)降维减少输入变量个数。试验对比分析了不同隐层神经元数量下输出值与期望值的相对误差及均方根误差,表明选用7个隐层神经元综合结果较优。以该机组存储的相关数据为例进行试验,结果表明模型测试得到的输出值与期望值的相对误差约为±2%,均方根误差约为13.4 MW。因此,本模型用于对机组出力进行预测具有较好的精确性与稳定性。
With the development of machine learning,neural network technology has become a solution to making analysis and prediction on massive data in coal-fired power plants.To facilitate the dispatching of power resources in power grid,it is key to predicting power unit output.Having modeled a 300 MW CFB based on BP neural network,the impact of abnormal data on the model accuracy was alleviated through data preprocessing,and the number of input variables in principal component analysis(PCA)was reduced through dimension reduction.Relative error and root-mean-square error between the output value and expected value under different numbers of hidden neurons were compared and analyzed in an experiment,and seven hidden neurons were selected for their comprehensive advantages.Studying the corresponding data of the unit in the experiment,the results show that the relative error between the output value and the expected value obtained by the model is around±2%,and the root-mean-square error is around 13.4 MW.Therefore,this model has good accuracy and stability in power unit prediction.
作者
韩义
张奇月
王研凯
于英利
付旭晨
荣俊
段伦博
HAN Yi;ZHANG Qiyue;WANG Yankai;YU Yingli;FU Xuchen;RONG Jun;DUAN Lunbo(Inner Mongolia Electric Power Research Institute,Inner Mongolia Electric Power(Group)Company Limited,Hohhot 010020,China;Key Laboratory of Energy Thermal Conversion and Control,Ministry of Education,Southeast University,Nanjing 210096,China)
出处
《华电技术》
CAS
2020年第12期1-6,共6页
HUADIAN TECHNOLOGY
基金
国家重点研发计划项目(2018YFB0605301)。
关键词
BP神经网络
机组出力
预测模型
相对误差
机器学习
BP neural network
power unit output
prediction model
relative error
machine learning
