Five models(LSVR,PSVR,RSVR,SSVR,and RF)were constructed by using two statistical learning algorithms(Support Vector Regression and Random Forest),and their performance in predicting the relationship between the high level heat generation of typical power coals and the industrial analysis data was evaluated.The results showed that the RSVR and RF models were able to accurately predict the high-level heat generation,especially within HHVd of 26.00～28.00 MJ/kg and Vd of 28～34%.The mean percentage errors(MAPE)of the RSVR and RF models were 0.97%and 0.96%,respectively.Different types of power coals were selected to verify the usability and application range of the models,and by comparing the absolute percentage errors with various types of coals,it can be found that the Random Forest model generally shows better adaptability and stability.
关键词
高位发热量预测/机器学习/动力煤/支持向量回归(SVR)/随机森林
Key words
higher heating value prediction/machine learning/bituminous coal/support vector regression(SVR)/random forest
维普
万方数据