Machine learning multi-step-ahead modelling with uncertainty assessment

Abstract

This study presents a strategy for multi-step-ahead Identification of robust machine learning (ML). Hence, we focus on the disparity between standard single-step prediction models and the requirement for multi-step forecasting, which is crucial for Model Predictive and Optimization schemes. This work explores how the proposed muti-step-ahead strategy can diminish the prediction uncertainty compared to the traditional single-step approach. The paper evaluates the multi-step Identification with uncertainty assessment in different model architectures, including those based on recursive neural networks. A key aspect of the analysis is the application of these models to a polymerization reactor, a standard benchmark in algorithm evaluation. The results reveal that multi-step recursive models significantly reduce prediction uncertainty compared to single-step models, particularly when feedback mechanisms are involved. This study highlights the advantages of multi-step models and their potential benefits for control and optimization schemes.