Stochastic Learning of Energy System for Data-Driven Control in Manufacturing Process

Abstract

To overcome the 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 the majority of manufacturing plants. Data-driven control setups seem to be a novel idea to handle the energy efficiency of such complex systems, while machine learning is becoming well-known in the 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 a manufacturing system energy in an optimized way, where the Model Predictive Control loop delivers optimal solutions for each control time step. In the end, a numerical example is demonstrated to give a clear picture of the proposed modelling method potentials.