基于机器学习的工业蒸汽量预测方法

INDUSTRIAL STEAM PREDICTION METHOD BASED ON MACHINE LEARNING

锅炉燃料燃烧产生的蒸汽在工业火电厂中有非常重要作用,根据锅炉的工况,预测产生的蒸汽量,有利于实时监测锅炉燃烧效率。对锅炉工况数据进行预处理和特征工程提取出有用特征,对提取的特征构造多种机器学习算法模型,并将多模型进行融合。结果表明:多种机器学习算法融合后的模型优于单一模型的准确率,融合后的模型预测蒸汽量的均方根误差为0.106,为工业实时监测锅炉燃烧效率提供了重要参考依据。

The steam produced by boiler fuel combustion has a very important role in industrial thermal power plants.Predicting the amount of steam produced according to the working conditions of boiler is conducive to the real-time monitoring of boiler combustion efficiency.Useful features of the boiler data are extracted by preprocessing and feature engineering,a variety of machine learning algorithm models are constructed for the extracted features,and the multiple models are merged.The results show that the fused model of multiple machine learning algorithms is superior to the single model in accuracy,and the root mean square error of steam predicted by the fused model is 0.106,providing important reference for the real-time industrial monitoring of boiler combustion efficiency.