A Granular Computing-Based Hybrid Hierarchical Method for Construction of Long-term Prediction Intervals for Gaseous System of Steel Industry

Byproduct gaseous energy is crucial to the iron-steel manufacturing process, where the tendencies of its generation and consumption can be deemed as a significant reference for scheduling production and decision-making. Besides the requirements imposed on numeric prediction, practical applications also demand that the result be represented in terms of intervals expressing the reliability of prediction outcomes. Meanwhile, prediction intervals should cover a long period of time for delivering more information on future long-term trends. Bearing this in mind, in this study, a Granular Computing-based hybrid hierarchical method is proposed for constructing long-term Prediction Intervals (PIs), in which the horizontal modelling gives rise to long periods of prediction, and the vertical one extends them to the interval-valued format. Information granules are hierarchically distributed over single data and then on industrial features-based segments. Considering the criteria of coverage and specificity as sound performance indexes of the model, a suite of optimization problems is formulated and solved by involving Particle Swarm Optimization (PSO). Experimental studies demonstrate that the proposed approach exhibits better performance when compared with the performance reported for other commonly encountered methods.