基于可解释关系模型的SCR入口气温预测OA

The proportion of new energy in the power generation in the whole society is constantly increasing.To shave peaks and fill valleys,the thermal power plants must increase their operating time in the low-load range,which has a negative effect on the denitrification of selective catalytic reduction(SCR).Therefore,accurate prediction of SCR gas temperature(SCRIGT)at the inlet is crucial.Firstly,using the operating parameters of the power plant as input and the ratio of power to SCRIGT as output,the XGBOOST model was used for prediction to obtain SCRIGT.The results show that for two different types of thermal power units,the average absolute percentage error(MAPE)is 3.07%and 2.49%,respectively.Then,the local interpretable model agnostic explanations(LIME)was applied to explain the prediction result of XGBOOST model,which finds a linear relationship between load and load∙SCRIGT-1 with R-squared of 0.994,based on which an interpretable relationship model was constructed.Finally,the comparative analysis shows that the mean absolute percentage errors of the interpretable relationship model have been improved to 0.68%and 0.97%,respectively.