Gaussian Processes Regression based Energy System Identification of Manufacturing Process for Model Predictive Control

Abstract

To overcome environmental impacts of a manufacturing factory over its life cycle, the role of sustainable energy effectiveness is vital. For this reason, implementing energy conservation technologies to empower energy efficiency has become an important business for majority of manufacturing plants. Data driven control set ups seem to be a novel idea to handle energy efficiency of such complex systems, while machine learning is becoming well-known in system engineering community. In this paper, a new approach together with optimal control application is considered to open promising energy saving ideas through investigating machines of a factory using machine learning, specifically, Gaussian Processes Regression (GPR), where the model is built by correlating the dynamics, complexity, and interrelated energy consumption recordings. We connect the idea with controlling of a manufacturing system energy in optimized way, where Model Predictive Control loop delivers optimal solutions for each control time step. In the end, numerical example is demonstrated to give a clear picture of the proposed modeling method potentials.