基于分位数回归的煤炭发热量预测

Prediction method of coal calorific value based on quantile regression

  • 摘要: 目前应用较多的煤炭发热量预测模型以传统的线性回归模型为主,但存在难以表达较复杂的自变量和因变量关系、需要数据服从特定的分布假设、对异常值敏感等问题。针对上述问题,提出了基于分位数回归的煤炭发热量预测方法。选取全水分、灰分、挥发分等容易测量的煤炭工业分析指标,分别应用线性分位数回归和分位数回归森林2种分位数回归方法对煤炭发热量进行预测,并与传统的线性回归方法进行对比。结果表明:线性回归给出的煤炭发热量预测值仅是1个条件均值,而通过分位数回归能够给出煤炭发热量预测值的范围;分位数回归森林的预测效果优于线性回归和线性分位数回归方法;全水分对于煤炭发热量预测的重要程度远大于灰分和挥发分;全水分对低发热量煤炭的发热量预测影响大,对高发热量煤炭的发热量预测影响小;挥发分和灰分对低发热量煤炭的发热量预测影响小,对高发热量煤炭的发热量预测影响大。

     

    Abstract: At present, the traditional linear regression model is mainly used to predict the calorific value of coal. But it is difficult to express the complex relationship between independent variables and dependent variables. The model needs data to obey specific distribution assumptions. And the model is sensitive to abnormal values. In view of the above problems, a prediction method of coal calorific value based on quantile regression is proposed. The method selects the coal industry analysis indicators that are easy to measure, such as total moisture, ash and volatile matter. The method uses two quantile regression methods, linear quantile regression and quantile regression forest, to predict the calorific value of coal. The results are compared with that of the traditional linear regression method. The results show that the predicted value of calorific value of coal given by linear regression is only a conditional mean value. But the range of predicted value of calorific value of coal can be given by quantile regression. The prediction effect of quantile regression is better than linear regression and linear quantile regression. The importance of total moisture for the prediction of calorific value of coal is much greater than that of ash and volatile matter. Total moisture has great influence on the prediction of calorific value of low calorific value coal. But total moisture has little influence on the prediction of calorific value of high calorific value coal. Volatile matter and ash have little influence on the prediction of calorific value of low calorific value coal. But volatile matter and ash have a great influence on the prediction of calorific value of high calorific value coal.

     

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