Long-term prediction enhancement based on multi-output Gaussian process regression integrated with production plans for oxygen supply network

Abstract

The variation tendency of oxygen demand plays a crucial role in planning and scheduling in the steel industry. Traditional prediction methods merely utilize historical data without considering any future production information. In steel enterprises, the production plan is a kind of easy-to-obtain future production information, which specifies the load adjustment, start-up, and shutdown of equipment in the future. In this paper, a prediction enhancement method based on multi-output Gaussian process regression (MOGPR) for the oxygen demand prediction is first developed. The proposed method models the correlations over three data sources, i.e., the plan-based oxygen prediction, the history-based oxygen prediction, and the oxygen measurement, and transfers the meaningful knowledge between them. Two long-term oxygen demand prediction methods integrated with prediction enhancement are further proposed. Finally, two industrial case studies are used to compare and demonstrate the effectiveness and performance of the proposed methods.