Machine learning–based predictive control for energy-efficient manufacturing systems

Abstract

Abstract. The fact that operational costs and the environmental impact are increasing is what has made energy consumption in manufacturing systems a serious issue. In this paper, a machine learning (ML)-based predictive control model is introduced to enhance energy efficiency in the contemporary manufacturing settings. The offered solution combines predictive models based on data and Model Predictive Control (MPC) to optimize the performance of the systems in real time. Machine learning algorithms are used to predict the energy demand, process dynamics, and disturbances, as well as to make decisions proactively. Industrial case studies confirm the validity of the framework, showing great progress in terms of energy efficiency, productivity, and stability of operations.