Understanding the future flow variation of byproduct gas is very crucial for energy
scheduling in steel industry. An accurate prediction of the tendencies is significantly
beneficial for raising the economic profits of steel enterprise. Given that most existing
techniques focus on short term or numeric prediction that can hardly meet the practical
requirements on the predicting horizon, the guidance effect of the results imposing on
energy scheduling is limitative. In this study, a granular computing (GrC)-based method for …