INTELLIGENT PREDICTIVE CONTROL FOR ENHANCING INDUSTRIAL MOTOR ENERGY EFFICIENCY

Abstract

Industrial electric motors account for a significant portion of global energy consumption in manufacturing and process industries. Improving motor energy efficiency has become a critical objective for reducing operational costs, minimizing environmental impact, and enhancing industrial sustainability. This paper presents an Intelligent Predictive Control (IPC) framework designed to optimize the performance and energy utilization of industrial motor systems. The proposed approach integrates predictive modeling, realtime sensor data acquisition, and machine learning algorithms to anticipate load variations and adjust motor operating parameters dynamically. By continuously analyzing historical and current operational data, the system predicts future motor behavior and implements optimal control actions to reduce energy losses while maintaining desired performance levels. The intelligent controller enhances speed regulation, torque management, and power factor correction, leading to improved operational efficiency. Simulation-based evaluations demonstrate that the proposed predictive control strategy significantly reduces energy consumption, minimizes unnecessary motor stress, and extends equipment lifespan compared with conventional control techniques. Furthermore, the framework supports adaptive decision-making under varying industrial conditions, ensuring reliable and cost-effective motor operation. The findings indicate that Intelligent Predictive Control offers a promising solution for achieving energy-efficient industrial automation and sustainable manufacturing practices.